Machine Learning Engineer

About The Role The role owns building, training, and deploying machine learning models that run in production — not prototypes that die in a notebook. The team ships models that serve real traffic daily, where accuracy, latency, and cost all matter. This is a hands-on engineering role: designing training pipelines, optimizing inference, and building the MLOps infrastructure that keeps models healthy at scale. The role reports into an applied ML org and works directly with data engineers, backend teams, and product stakeholders. Key Responsibilities Design, train, and productionize ML models (classification, ranking, NLP, and LLM-based systems) using PyTorch or TensorFlow Build and maintain scalable data and feature pipelines in Python and PySpark, ensuring consistency between training and serving environments Deploy and version models on AWS or GCP using SageMaker, Vertex AI, or Kubernetes-based serving stacks (TorchServe, Triton, KServe) Optimize model inference for latency and cost: quantization, distillation, batching, and GPU utilization tuning Instrument model monitoring for data drift, performance regression, and automated retraining triggers using tools like Evidently or custom pipelines Run rigorous offline and online experiments (A/B tests, shadow deployments) and communicate results clearly to technical and non-technical stakeholders Contribute to ML platform improvements: CI/CD for models, experiment tracking (MLflow, W&B), and shared evaluation frameworks What We Are Looking For 3–7 years of experience in machine learning engineering or applied ML, including multiple models shipped to production Expert-level Python and hands-on depth with PyTorch or TensorFlow; strong software engineering fundamentals (testing, code review, system design) Production experience with cloud ML infrastructure: AWS (SageMaker, ECS/EKS) or GCP (Vertex AI, GKE) Solid grasp of ML fundamentals: evaluation metrics, regularization, class imbalance, and the gap between offline metrics and real-world performance Experience with MLOps tooling: Docker, Kubernetes, CI/CD pipelines, and experiment tracking systems BS in Computer Science, Statistics, Engineering, or equivalent practical experience; MS a plus Bonus: experience fine-tuning or serving LLMs, GPU optimization (CUDA, TensorRT), Rust/Go for inference services, or contributions to open-source ML projects