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About WPP Media WPP is the trusted growth partner for the world's leading brands. With exceptional talent, trusted data and intelligence, and world-class partnerships – all united by our pioneering agentic marketing platform, WPP Open – we help clients navigate change, capture opportunity, and deliver transformational growth. WPP Media is WPP's AI-driven media operating unit, bringing together media, data, and partnerships to deliver creative personalisation at scale. Connected through WPP Open and powered by Open Intelligence, clients see exactly where, how, and why their media investment is working. For more information, visit wppmedia.com . Role Purpose The Platform Operations Director is responsible for ensuring that solutions developed and released for the Solutions team are operationally ready, properly tested, clearly documented, effectively released, and provide support once live. The role acts as the bridge between delivery, business readiness and BAU operations. It supports the transition of work from project delivery into stable operational use, ensuring that users, stakeholders and support teams understand what has changed, how to use it, how to raise issues, and how ongoing support will be managed with appropriate priority, impact and SLAs This role is particularly important as Solutions Integration moves from a more reactive service model into a structured product and agile delivery function. The Platform Operations Director owns QA, release management, documentation, stakeholder communication, support processes, training, adoption and post-release improvement including BAU bugs and triage. The role works closely with Product Owners, Scrum Master, Engineering, Data, Reporting, QA, Platform Operations, and business stakeholders to make sure delivery does not stop at "built", but continues through testing, launch, adoption, support and measurable operational value. Senior / Specialist-Level Expectations As a Director within the team, this role is expected to help define and improve the operating standards around BAU management, QA coordination, release readiness, business readiness and service transition. Manage and proactively manage risk, resolution SLAs and ensure we are adhering to platform operational best practices. The Platform Operations Director should be able to identify operational risks early, challenge whether a solution is genuinely ready for release, and recommend practical improvements to processes, documentation, testing, support models and stakeholder communications. For smaller initiatives, the role should be able to advise Product Owners and delivery teams on the appropriate level of QA, training, release planning, handover and BAU support required. For larger or more complex initiatives, the role should escalate risks, dependencies or readiness gaps to the Product Manager Lead, Scrum Master, Lead Engineer / Solutions Architect or relevant leadership stakeholders. Key Responsibilities 1. BAU Operations Management Own and coordinate BAU operational processes for assigned platforms, tools, reports, workflows and solutions across all Media solutions. Monitor recurring issues, support trends, operational risks and user pain points. Triage and prioritise BAU requests, incidents, defects and enhancement requests. Ensure BAU work is captured through the agreed intake and backlog process rather than unmanaged emails, verbal requests or fragmented ticket routes. Work with Product Owners and the Scrum Master to distinguish between BAU support, defects, enhancements and new product requirements. Maintain visibility of BAU demand and help identify where recurring issues should become product improvements or engineering fixes. Support continuous improvement of support processes, documentation and operational workflows. This links directly to the team's wider shift away from fragmented request routes and towards clearer delivery rhythms, structured backlogs and improved operational process discipline. The existing documentation notes the need for clearer team rhythms, sprint cadences, retrospectives and mentoring as part of the transition plan . 2. QA Coordination and Testing Readiness Coordinate QA activity across delivery workstreams, ensuring testing requirements are understood before development begins. Work with Product Owners to ensure acceptance criteria, test scenarios and success measures are clearly defined. Support test planning across functional testing, regression testing, data validation, reporting checks and user acceptance testing. Ensure defects are logged, prioritised, tracked and resolved through the agreed delivery process. Work with engineers, analysts and business users to validate that solutions meet functional, operational and reporting requirements. Support the creation of reusable QA checklists, test scripts and validation templates. Ensure solutions are not released without appropriate testing evidence and business sign-off. This is consistent with the Product Owner responsibilities already defined around testing requirements, success criteria, UAT coordination, business validation, release readiness and operational handover . The Platform Operations Specialist would support and operationalise those activities. 3. Business Readiness and Adoption Ensure business users and operational teams are ready to adopt new or changed solutions. Coordinate training, guidance, demos, FAQs, release notes and user communications. Work with Product Owners to identify impacted teams, users, processes and workflows. Confirm that business teams understand what is changing, why it is changing, when it is happening and what action is required. Support stakeholder communications before, during and after release. Gather early feedback from users and ensure issues are captured and prioritised appropriately. Help ensure delivered solutions are embedded into day-to-day operations, rather than remaining underused or misunderstood. The handover documentation highlights the importance of clear communication with users, proper release notes, demonstrations and adoption support for new features and solutions . This role should own the operational discipline around making that happen consistently. 4. Release Readiness and Service Transition Own or coordinate release readiness checklists for assigned solutions. Ensure each release has appropriate testing evidence, documentation, support ownership, training material and stakeholder communication. Coordinate operational handover from delivery teams into BAU support. Confirm that runbooks, troubleshooting guides, known issues and escalation routes are in place. Work with engineering and data teams to ensure monitoring, alerting or manual checks are defined where required. Support go-live planning, release scheduling and post-release hypercare. Track post-release issues and ensure learnings are fed back into future delivery cycles. This role should become the person asking: "Are we actually ready to put this live, support it, explain it, and measure whether it worked?" 5. Documentation and Knowledge Management Maintain clear operational documentation for platforms, reports, tools, processes and workflows. Support the creation of user guides, SOPs, runbooks, release notes, FAQs and support documentation. Ensure documentation is stored in the agreed location and kept up to date. Work with Product Owners, engineers and analysts to document key dependencies, business rules, known limitations and support processes. Help reduce reliance on individual knowledge by making processes and system behaviour transparent. Support onboarding of new team members by maintaining accessible knowledge resources. This is important because the team is moving away from legacy ways of working and informal knowledge transfer into a more structured operating model. 6. Platform and Process Governance Support the consistent use of agreed tools, workflows, ticketing processes and delivery governance. Help ensure work is captured, categorised and progressed through the correct routes. Maintain standards for BAU tickets, defects, release requests, support issues and operational change. Identify process gaps, duplicated effort or inconsistent ways of working. Recommend improvements to BAU processes, support models, QA standards and release governance. Support reporting on operational performance, support demand, defect trends and release readiness. Help ensure changes are controlled, documented and aligned to agreed team practices. This aligns with the documented need to improve proactive behaviour, structured product management, backlog grooming, stand-ups and retrospectives across the team . 7. Stakeholder and Cross-Team Coordination Work closely with Product Owners, Engineering, Data, Reporting, QA, Scrum Master and business users. Coordinate with Activation, Operations, Channel Development, Commercial and other impacted teams where relevant. Ensure operational dependencies are understood and managed before release. Help stakeholders understand support routes, known issues, timelines and release impacts. Escalate operational risks, unresolved defects, readiness gaps or business adoption concerns. Support regular BAU and platform operations updates for leadership where required. The existing Product Owner documentation already describes the importance of coordination across engineering, data, reporting, platform, operations and business teams . The Platform Operations Specialist should provide the operational control layer that supports this. Skills and Experience Essential Experience in platform operations, service transition, QA coordination, business readiness, delivery operations or technical operations. Strong understanding of BAU support processes, release management and operational handover. Experience coordinating UAT, defect tracking, test planning or business validation activity. Ability to create clear documentation, user guides, process notes, release communications and operational runbooks. Strong stakeholder management skills, with the ability to work across technical and non-technical teams. Good understanding of agile or product-led delivery environments. Ability to identify operational risks, readiness gaps, support issues and process improvements. Strong organisational skills and attention to detail. Confident using Jira, Confluence, ServiceNow, Azure DevOps or similar delivery/support tooling. Ability to communicate clearly with users, delivery teams and senior stakeholders. Desirable Experience working in media, advertising, ad tech, martech, reporting, data or platform environments. Experience supporting reporting platforms, dashboards, data products or operational workflow tools. Understanding of data validation, reporting QA or campaign performance data. Familiarity with cloud-based platforms such as GCP, AWS, BigQuery or Snowflake. Experience working with engineering, data engineering or analytics teams. Experience supporting transformation from reactive support models into structured product or agile delivery models. Familiarity with WPP, WPP Media, Choreograph or similar enterprise platform environments would be beneficial. #LI-Promoted Please read our Privacy Notice for more information on how we process the information you provide. Find Jobs in United Kingdom on Arbeitnow

Transforming factory scans into digital reality At RIIICO, we're reimagining how factories adapt to change. Our AI-powered software makes complex production environments editable, interactive, and collaborative, turning manufacturing planning into something anyone can engage with, not just the experts. We're looking for a 3D Data Operator to help power the foundation of our digital factory experiences. No point cloud experience needed, just an eye for detail and curiosity about 3D. In this role, you'll work at the intersection of 3D reality and AI, transforming raw point cloud data into accurate digital twins of real-world production environments. From data preparation to model generation and delivery, you'll help ensure quality, consistency, and continuous improvement across our data pipeline. Your daily work Process 3D scan data (point clouds) and transform it into usable digital factory models using our internal tools and workflows. Run basic pipeline commands for data preparation and AI processing. Configure and verify model outputs to ensure quality, consistency, and usability for downstream applications (e.g., Nvidia Omniverse, Siemens Plant Simulation and others). Collaborate with AI-assisted tools to validate and correct automated results. Flag and troubleshoot anomalies or edge cases in raw or processed data, and document issues clearly (e.g., as Git issues). Track and report your weekly processing output. Continuously improve workflows and documentation based on team feedback and real-world challenges. What we are looking for Strong attention to detail and ability to reliably handle repetitive tasks with care. Self-motivated and dependable: you take responsibility and get things done. Comfortable following defined workflows and standards, and asking for help when something is unclear. Team player with a proactive mindset: you enjoy working in a collaborative team. Fast learner: no prior experience with point clouds required; you're comfortable picking up new tools and workflows quickly. Basic digital literacy: you're confident navigating structured software tools, browser-based interfaces and Microsoft Office. Bonus: Familiarity with 3D environments (e.g., through gaming, CAD, or similar), or a general spatial intuition. English is a must; proficiency in German is a plus for clear communication with the team. Please note: A valid work permit or residence permit for Germany is required for this position. This is a 12-month fixed-term position, with the opportunity to extend or move to a permanent contract based on performance and business development. Benefits Additional vacation days Flexible working hours Hybrid office culture Public transport subsidy Why You Should Join Build something that makes a tangible difference in the world's most important sector: manufacturing. Help shape a product that is being built from first principles. Collaborate with a sharp, hands-on team that values speed, clarity, and ownership. Backed by top international investors and preparing to scale. Find Jobs in Germany on Arbeitnow

FUNCTION & DUTIES:Under the Assistant Director of Complementary Services, the Consultant will work in close collaboration with the other special education consultants in all areas of special education to provide services to special needs students in Inuktitut and to contribute to the in-service training and course sessions of Inuit teachers. Assessment and diagnosis of students with suspected learning problems in the schoolsIdentification of students with emotional and/or behavioural problemsTraining and support for the special education teachers and counsellors within the BoardOn-going development of culturally appropriate diagnostic tools and remedial materialIn-service and teacher training in the areas of educationInvolvement in on-going research concerning language of instruction. QUALIFICATIONS:Valid teaching diploma in the province of Quebec or close to graduating from the Kativik-McGill Teacher Training ProgramAt least three (3) years of teaching experience with children with special needs. The Board may, at its discretion, waive any or all of the afore-mentioned qualifications if it finds a suitable candidate who is a beneficiary of the James Bay and Northern Québec Agreement (JBNQA) and who accepts, as a condition of employment, to follow a training plan determined by the Board. REQUIREMENTS:Fluency in written and spoken Inuktitut and a good command of a second language (French or English)Knowledge of diagnostic and remedial tools in InuktitutAvailable to travel regularly. CLASSIFICATION / SALARY / BENEFITS:In accordance with the SPPENOM collective agreement for professionals. This position belongs to job category 2104: from $56,670 TO $105,432 annually depending on qualification and experience. All benefits are prorated based on the percentage of the task.

If you are committed to public service, enjoy collaborating with others, share our values and have a desire to learn and grow, join The City of Calgary. City employees deliver the services, run the programs and operate the facilities which make a difference in our community. We support work-life balance, promote physical and psychological safety, and offer competitive wages, pensions, and benefits. Together we make Calgary a great place to make a living, a great place to make a life. The City is committed to fostering a respectful, inclusive and equitable workplace which is representative of the community we serve. We welcome those who have demonstrated a commitment to upholding the values of equity, diversity, inclusion, anti-racism and reconciliation. Applications are encouraged from members of groups that are historically disadvantaged and underrepresented. Accommodations are available during the hiring process, upon request. As the Vehicle and Equipment Training Coordinator, you will be scheduling all vehicle and equipment training for Mobility operations, including recertifications, remedial training, equipment upgrades, and new staff onboarding. In this position, you will support multiple operational groups across 15 locations and 20 shifts and also support a variety of adult learning activities related to orientation, training rollouts, learning initiatives, and employee development. You will also maintain training records and supports compliance with training, CUPE 37 collective agreement, and MOU requirements. Primary duties include:Coordinate one-on-one vehicle and equipment training across Mobility operational groups by creating efficient training schedules, prioritizing requests, balancing operational readiness, trainer availability, vendor capacity, equipment constraints, employee release time, and shift requirements.Work with internal leaders, employees, Trainers, and vendors to clarify training needs, provide updates, resolve scheduling issues, manage expectations, and adjust training plans based on changing operational priorities.Input relevant course data into the LMS and use LMS information to create training gap reports that support leader decision-making.Track training activity by division, equipment type, employee qualification status, and training priority to support compliance and seasonal readiness goals. Develop and maintain training status tracking tools, dashboards, and summary reports that give leaders visibility into training progress, outstanding requests, queue status, employee training needs, and upcoming training priorities.Coordinate prerequisite, compliance-based, recertification, corrective, and seasonal training needs, including the rollout of updated training documentation and verification of employee training acknowledgements when operational, safety, or equipment-related issues are identified. Qualifications A completed 2 year Diploma in Business Administration, Adult Learning, Event Management, or a related field and at least 3 years of related experience.Experience in an organizational effectiveness or learning and development role is required.Intermediate level of proficiency in Microsoft Office (Word, PowerPoint and Excel) is also required.Proficiency working with PeopleSoft Human Capital Management (HCM), Sum Total Learning Management System, SharePoint and Livelink would be considered an asset.Success in this position requires the following:Customer service and communication skills.Ability to work with people at all levels of the organization, solve problems both independently and as part of a larger team.Strong organizational skills and attention to detail are essential. Pre-employment Requirements Successful applicants must provide proof of qualifications.Workstyle This position may be eligible to work from home for at least part of the time as one of several flexible work options available to City employees. These arrangements depend on the operational requirements of the role,

FUNCTION & DUTIES:Under the supervision of the Assistant Director General, the Centre Director is the senior administrator of the Board in his/her community and is responsible for all aspects of the organization and administration of the community school and its related services. Other duties include:Responsible for the implementation of Board and local policy as determined by the Commissioners and the local Education Committee.Is the liaison between the community and theResponsible for the organization and supervision of the physical plant including school, residences, shops, warehouse, vehicles and equipment.Is the Secretary-Treasurer of the local EducationSupervise, mentor and manage employees under their responsibility.Is responsible for all aspects of the organization and administration of Board activities within his/herQUALIFICATIONS:Undergraduate degree in a relevant field of study certifying a minimum three (3)-year university program or hold a senior or senior executive staff position, excluding a position as manager, in a board.The Board may, at its discretion, waive any or all of the aforementioned qualification requirements if it finds a suitable candidate who is a beneficiary of the James Bay and Northern Québec Agreement and who accepts, as a condition of employment, to follow a training plan determined by the Board. REQUIREMENTS:Fluency in Inuktitut and either French or EnglishA minimum of three (3) years of experience in the field of administration or education or equivalent relevant experience recognized by the BoardMust have been a full-time resident of an Inuit community of Nunavik for at least ten (10) yearsAn understanding of the Quebec Education system and Chapter 17 of the James Bay and Northern Quebec Agreement is an asset. CLASSIFICATION / SALARY / BENEFITS:Class 00-08 as per the working conditions of managers at KI: from $100,606 to $134,139 annually depending on qualifications and experience. All benefits are prorated based on the percentage of the task. No housing available for this position

Education: Earned doctorate degree. Engineering, general. Area of work experience: Teaching. Research. Personal suitability: Accurate. Dependability. Efficient interpersonal skills. Excellent oral communication. Excellent written communication. Flexibility. Initiative. Judgement. Organized. Reliability. Team player. Innovation. Screening questions: Are you available to start on the date listed in the job posting?. Do you have experience working in this field?. Do you have the required certifications listed in the job posting?. Do you meet the language requirements listed in the job posting for the position (English or French)?. Experience: Experience an asset. Workplace information: Hybrid. Health benefits: Disability benefits. Health care plan. Paramedical services coverage. Vision care benefits. Financial benefits: As per collective agreement. Group insurance benefits. Life insurance. Pension plan. Long term benefits: Long-term care insurance. Maternity and parental benefits. Other benefits: Learning/training paid by employer. On-site amenities. On-site daycare available. On-site recreation and activities. Other benefits. Paid time off (volunteering or personal days). Team building opportunities. Parking available. Travel insurance. Wellness program. Support for persons with disabilities: Provides physical accessibility accommodations (for example: ramps, elevators, etc.). Provides visual accessibility accommodations (for example: braille, screen readers, etc.). Provides auditory accessibility accommodations (for example: transcription software, teletypewriters, etc.). Offers mentorship, coaching and/or networking opportunities for persons with disabilities . Provides awareness training to employees to create a welcoming work environment for persons with disabilities. Applies accessible and inclusive recruitment policies that accommodate persons with disabilities . Support for Indigenous people: Offers mentorship, coaching and/or networking opportunities for Indigenous workers . Develops and maintains relationships with indigenous communities, indigenous-owned businesses and organizations . Provides cultural competency training and/or awareness training to all employees to create a welcoming work environment for Indigenous workers . Supports for visible minorities: Applies hiring policies that discourage discrimination against members of visible minorities (for example: anonymizing the hiring process, etc.). Offers mentorship programs that pair members of visible minorities with experienced employees. Provides diversity and cross-cultural training to create a welcoming work environment for members of visible minorities.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Role Summary Nebius Token Factory is building production-grade inference for open-source, customer, and internally optimized models. This role owns the path from model artifact to fast, reliable, cost-efficient endpoint. The work sits at the intersection of model internals, inference engines, serving architecture, benchmarking, and production deployment. What You Will Do Own a platform-level optimization pillar such as quantization, speculative decoding, KV -cache optimization, engine selection, or long-context serving. Create reusable evaluation, benchmarking, and rollout frameworks across multiple model families. Provide technical direction to MLEs, Applied Scientists, GPU kernel engineers, and infrastructure engineers on shared performance goals. Optimize LLM and VLM endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems. Build and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV -cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. Build reproducible benchmark harnesses for TTFT , TPOT , tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Partner with GPU kernel engineers and platform engineers to diagnose bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Write clear design docs, performance reports, rollout plans, and customer-facing technical explanations. Required Qualifications Strong Python and PyTorch engineering skills. Hands-on experience deploying or optimizing LLM, VLM , or high-throughput transformer inference systems. Practical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent internal systems. Strong understanding of transformer inference bottlenecks, including KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs. Strong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams. Preferred Qualifications Experience with quantization-aware training, post-training quantization, FP8 , INT8 , INT4 , NVFP4 , MXFP4 , AWQ , GPTQ , SmoothQuant, or related techniques. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration methods. Experience with agentic workloads, including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. CUDA or Triton familiarity, even if the role is not primarily a kernel-engineering role. Open-source contributions to vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related projects. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know. Find more English Speaking Jobs in United Kingdom on Arbeitnow

About us EggAI is a fast-growing AI consulting and development startup. We build agentic AI solutions for enterprise clients. We are growing in several European markets. The role You will lead our clients through the organisational and operational changes needed to establish a shared AI platform. The platform has two parts: an AI Control Plane that governs what agents may do and records the evidence, and a managed AI Runtime where the agents run. Both integrate with the client's identity, security, delivery and audit services, and both need owners, approval routes and support arrangements on the client side. You will own delivery outcomes from mobilisation through production release and operating handover, acting as the client sponsor's day-to-day counterpart. The role reaches across engineering, business leadership, security, risk, compliance and operations. You will bring these groups together to agree how the platform is governed and supported, and what evidence they need for assurance in a regulated environment. You will lead an EggAI delivery team with a mix of Platform Engineering roles and skills. Client application teams develop the business use cases; you lead AI platform delivery and the changes needed for teams to adopt and operate it. Why this role Most organisations are still working out how to let AI agents act safely in production: who approves what an agent may do, how its actions are evidenced, and who answers when something goes wrong. There is no settled playbook yet, especially in regulated industries. In this role you will help write it. The operating models, approval routes and assurance practices you design with our clients will shape how they run agentic AI for years, and they will become the basis of how EggAI delivers across Europe. You will work at the point where AI capability meets enterprise reality, with direct access to client sponsors and EggAI leadership, and with real influence over how a growing company builds its delivery practice. Responsibilities - Own the delivery plan and milestones, keeping commitments achievable within the agreed scope, budget and team capacity. - Secure the client commitments that delivery depends on. Agree who will provide access, complete service changes and make decisions, then resolve delays with the responsible teams. - Design the target operating model with the client. Define who owns the platform service, maintains policies and software, supports the runtime and approves changes, with agreed escalation routes. - Run milestone acceptance. Submit evidence for each milestone, secure recorded client decisions within agreed review periods and escalate overdue or disputed decisions. - Lead change control with the sponsor and EggAI leadership. Agree scope, capacity, cost and date changes, including proposals for additional services or extended support, before committing the team. - Secure production readiness. Confirm the service owners and funded operating cover from the operating model are in place before go-live, escalating any gap that prevents release. - Lead go-live coordination and the intensive support period after release. Bring client operations and application teams together to resolve incidents and agree when normal support can take over. - Lead platform adoption and training with the technical team, preparing client teams to use shared controls and follow the agreed delivery processes. - Run operating handover through practical exercises. Secure acceptance of ongoing duties and agree any further support needed before the current engagement ends. - Give sponsors a clear account of progress and delivery risks through weekly delivery reviews, regular demonstrations and sponsor reviews. Report spending, forecast work and adoption results so they can make timely decisions. What You Bring - You have led technology programmes into production in a regulated environment, including operational readiness and handover to receiving teams. - You have designed and introduced operating models, turning agreed responsibilities into working approval, support and service-management processes. - You have led delivery across engineering teams, client departments and external partners, including influencing senior sponsors and resolving competing priorities. - You have managed budgets and contractual scope in client delivery, including negotiating changes and shaping additional work. - You understand cloud platforms, security and service operations well enough to challenge plans and dependencies with technical specialists. - You communicate clearly and can lead workshops that turn disagreement or uncertainty into decisions and action. Nice to have Delivery in financial services or banking. AI platforms or agentic AI. Platform engineering, identity, security controls or audit and assurance work. Cloud platform delivery using Azure (preferred), AWS or Google Cloud, and AI platform services such as Microsoft Foundry. German is a strong advantage, as many of our clients are in the DACH region. Practical details - Hybrid working and client-site attendance will depend on the engagement. - You will need to complete the background and security checks required by the client. We'd love to hear from you! If your profile looks like a good match we'll be in touch to organise a first stage interview. Please note, due to the high volume of candidates, we cannot offer personalized feedback to each candidate. Find more English Speaking Jobs in United Kingdom on Arbeitnow

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions. You will evaluate model- and engine-level optimizations together with distributed inference designs, including request routing, scheduling, prefill/decode coordination, and multi-node GPU execution. Your improvements will be validated through reproducible benchmarks and safe production rollouts under real-world workloads. Your responsibilities Own optimization projects for specific model families, customer endpoints, or serving backends. Compare inference engines and recommend serving configurations suited to individual workloads. Diagnose model-quality and performance regressions during production rollouts. Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. For prefill–decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture. Design and improve LLM request routers and scheduling policies that balance worker utilization, queueing, request characteristics, and KV-cache locality while meeting latency and reliability targets. Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism—including wide expert parallelism (WideEP)—with attention to hardware topology, expert load balance, and communication overhead. Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets. Collaborate with GPU kernel and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Produce design documents, performance reports, rollout plans, and customer-facing technical explanations. Must-haves Strong engineering skills in Python and PyTorch. Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference. Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost. Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism. Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams. Nice-to-haves Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, or SmoothQuant. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or related inference acceleration methods. Experience supporting agentic workloads involving tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. Familiarity with CUDA or Triton; the role does not require kernel engineering to be the candidate's primary specialization. Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, or KServe. Implementation experience with cache-aware request routing, PDD, or WideEP, including diagnosing communication bottlenecks and load imbalance. Familiarity with GPU communication libraries and interconnects, such as NCCL, NVLink, InfiniBand, or RoCE. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know. Find more English Speaking Jobs in Germany on Arbeitnow

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Role Summary Nebius Token Factory is building production-grade inference for open-source, customer, and internally optimized models. This role owns the path from model artifact to fast, reliable, cost-efficient endpoint. The work sits at the intersection of model internals, inference engines, serving architecture, benchmarking, and production deployment. What You Will Do Own a platform-level optimization pillar such as quantization, speculative decoding, KV -cache optimization, engine selection, or long-context serving. Create reusable evaluation, benchmarking, and rollout frameworks across multiple model families. Provide technical direction to MLEs, Applied Scientists, GPU kernel engineers, and infrastructure engineers on shared performance goals. Optimize LLM and VLM endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems. Build and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV -cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. Build reproducible benchmark harnesses for TTFT , TPOT , tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Partner with GPU kernel engineers and platform engineers to diagnose bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Write clear design docs, performance reports, rollout plans, and customer-facing technical explanations. Required Qualifications Strong Python and PyTorch engineering skills. Hands-on experience deploying or optimizing LLM, VLM , or high-throughput transformer inference systems. Practical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent internal systems. Strong understanding of transformer inference bottlenecks, including KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs. Strong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams. Preferred Qualifications Experience with quantization-aware training, post-training quantization, FP8 , INT8 , INT4 , NVFP4 , MXFP4 , AWQ , GPTQ , SmoothQuant, or related techniques. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration methods. Experience with agentic workloads, including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. CUDA or Triton familiarity, even if the role is not primarily a kernel-engineering role. Open-source contributions to vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related projects. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know. Find more English Speaking Jobs in Germany on Arbeitnow

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions. You will evaluate model- and engine-level optimizations together with distributed inference designs, including request routing, scheduling, prefill/decode coordination, and multi-node GPU execution. Your improvements will be validated through reproducible benchmarks and safe production rollouts under real-world workloads. Your responsibilities Own optimization projects for specific model families, customer endpoints, or serving backends. Compare inference engines and recommend serving configurations suited to individual workloads. Diagnose model-quality and performance regressions during production rollouts. Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. For prefill–decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture. Design and improve LLM request routers and scheduling policies that balance worker utilization, queueing, request characteristics, and KV-cache locality while meeting latency and reliability targets. Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism—including wide expert parallelism (WideEP)—with attention to hardware topology, expert load balance, and communication overhead. Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets. Collaborate with GPU kernel and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Produce design documents, performance reports, rollout plans, and customer-facing technical explanations. Must-haves Strong engineering skills in Python and PyTorch. Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference. Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost. Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism. Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams. Nice-to-haves Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, or SmoothQuant. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or related inference acceleration methods. Experience supporting agentic workloads involving tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. Familiarity with CUDA or Triton; the role does not require kernel engineering to be the candidate's primary specialization. Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, or KServe. Implementation experience with cache-aware request routing, PDD, or WideEP, including diagnosing communication bottlenecks and load imbalance. Familiarity with GPU communication libraries and interconnects, such as NCCL, NVLink, InfiniBand, or RoCE. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know. Find more English Speaking Jobs in Netherlands on Arbeitnow

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Role Summary Nebius Token Factory is building production-grade inference for open-source, customer, and internally optimized models. This role owns the path from model artifact to fast, reliable, cost-efficient endpoint. The work sits at the intersection of model internals, inference engines, serving architecture, benchmarking, and production deployment. What You Will Do Own a platform-level optimization pillar such as quantization, speculative decoding, KV -cache optimization, engine selection, or long-context serving. Create reusable evaluation, benchmarking, and rollout frameworks across multiple model families. Provide technical direction to MLEs, Applied Scientists, GPU kernel engineers, and infrastructure engineers on shared performance goals. Optimize LLM and VLM endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems. Build and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV -cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. Build reproducible benchmark harnesses for TTFT , TPOT , tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Partner with GPU kernel engineers and platform engineers to diagnose bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Write clear design docs, performance reports, rollout plans, and customer-facing technical explanations. Required Qualifications Strong Python and PyTorch engineering skills. Hands-on experience deploying or optimizing LLM, VLM , or high-throughput transformer inference systems. Practical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent internal systems. Strong understanding of transformer inference bottlenecks, including KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs. Strong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams. Preferred Qualifications Experience with quantization-aware training, post-training quantization, FP8 , INT8 , INT4 , NVFP4 , MXFP4 , AWQ , GPTQ , SmoothQuant, or related techniques. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration methods. Experience with agentic workloads, including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. CUDA or Triton familiarity, even if the role is not primarily a kernel-engineering role. Open-source contributions to vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related projects. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know. Find Jobs in Netherlands on Arbeitnow