Machine Learning Engineer

About The Role

The role sits at the intersection of model development and production systems: taking ML workloads from prototype to monitored, reliable services running at scale.

The team ships models that support real product decisions daily, and this role will own critical pieces of that pipeline - from training and evaluation through deployment, serving, and continuous monitoring - working alongside data scientists, backend engineers, and platform teams.

Key Responsibilities

• Design, train, and evaluate production ML models - classification, ranking, NLP, and recommendation use cases - with clear ownership of accuracy, latency, and cost tradeoffs
• Build and maintain data pipelines in Python and PySpark/Spark for feature engineering, training datasets, and online/offline consistency
• Deploy models end-to-end using AWS SageMaker, Vertex AI, or Kubernetes-based serving stacks, including canary releases, shadow deployments, and rollback procedures
• Instrument model monitoring for data drift, feature skew, and performance regression; build automated alerting and retraining triggers
• Optimize inference performance: batching, quantization, distillation, and hardware-aware serving for latency-sensitive workloads
• Collaborate with data scientists and MLOps engineers to establish experiment tracking, model versioning (MLflow or equivalent), and reproducible training workflows
• Contribute to code reviews, architecture discussions, and ML engineering best practices across the team

What We Are Looking For

• 3–6 years of experience in machine learning engineering or applied ML, including multiple production model deployments
• Strong Python engineering skills; comfort with testing, code review, and software engineering fundamentals beyond notebooks
• Deep proficiency with PyTorch or TensorFlow, plus hands-on experience with scikit-learn and classical ML methods
• Production experience with at least one major cloud ML platform: AWS SageMaker, GCP Vertex AI, or Azure ML
• Solid grasp of ML fundamentals: evaluation design, regularization, bias-variance tradeoff, and distribution shift
• Experience with containerized ML services (Docker, Kubernetes) and workflow orchestration (Airflow, Kubeflow, or Dagster)
• Bonus: experience with LLM fine-tuning or serving (vLLM, Triton), real-time feature stores (Feast, Tecton), or a Master's/PhD in a quantitative field