Machine Learning Engineer

We're partnered with a venture-backed technology company using AI to solve complex, real-world operational challenges at scale. The team is building production AI systems that combine retrieval, ranking, decision-making, and generative models to automate highly complex workflows while maintaining reliability, explainability, and measurable business outcomes. This is an opportunity to join a high-calibre engineering team where you'll work across the full lifecycle of machine learning systems, from early-stage experimentation and model development through to deployment, monitoring, and continuous improvement in production. What You'll Be Working On Building and scaling machine learning infrastructure that enables rapid experimentation, benchmarking, evaluation, and deployment. Designing and developing AI systems for retrieval, ranking, classification, recommendation, and generative AI applications. Owning the end-to-end lifecycle of machine learning systems, from problem definition and model selection through production deployment and iteration. Creating robust evaluation frameworks, datasets, and validation methodologies to measure real-world performance and business impact. Developing ranking and decision-making systems that intelligently route tasks between automated systems and human operators. Building feedback loops and data flywheels that continuously improve model performance over time. Working closely with engineering, product, and domain experts to translate complex business problems into scalable AI solutions. Prototyping new AI capabilities from inception and evolving them into reliable production systems. Contributing to machine learning best practices across model development, experimentation, deployment, and monitoring. What We're Looking For 5+ years of software engineering experience, including at least 2+ years working directly with machine learning systems. Strong experience developing and deploying machine learning models into production environments. Proficiency with Python and modern machine learning frameworks such as PyTorch, TensorFlow, or JAX. Experience building large-scale AI systems involving retrieval, ranking, recommendation, search, classification, or generative AI. Strong understanding of machine learning evaluation, experimentation, benchmarking, and model performance optimization. Experience designing scalable distributed systems and production-grade software architectures. Ability to move comfortably between research, engineering, and product discussions. Strong communication skills and a bias toward ownership and execution. Nice to Have Experience working with LLMs, RAG systems, AI agents, or generative AI applications. Experience building experimentation platforms, evaluation frameworks, or ML infrastructure. Background in ranking systems, recommendation systems, search, or decision intelligence. Experience operating machine learning systems at scale with real-world users and production traffic. Previous experience mentoring engineers or leading technical initiatives. Show more Show less