Intermediate Machine Learning Engineer

What you'll do:

• Implement and extend ML platform capabilities (training jobs, inference services, batch and online serving) following team architecture, standards, and best practices.
• Develop and maintain components of the ML model development workflow (project structure, experimentation, versioning, reproducibility) to improve consistency and reuse across teams.
• Build and operate observability for models in training and production—monitoring, logging, and alerting for performance, quality, and drift—in collaboration with platform and SRE partners.
• Support optimized model deployments (scaling, resource allocation, inference tuning) to meet cost, quality, and SLA targets.
• Troubleshoot pipeline and serving issues, document solutions, and share learnings with the team.

What you'll need:

• Bachelor’s degree in computer science, engineering, mathematics, or another quantitative field.
• Practical experience with ML platform components (e.g., feature pipelines, model registries, training and inference workflows).
• Solid software engineering fundamentals: clean code, testing, CI/CD, and maintainable system design.
• Python, PySpark, and SQL. Exposure to Java is a plus.
• Experience with Kubernetes, Databricks, Terraform, Azure DevOps (Git), Azure Cloud, and ML frameworks/libraries such as Scikit-learn, PyTorch, TensorFlow, ONNX, and serving tools (BentoML, Kedro, Seldon, KServe, Triton Inference Server, etc.).
• Comfort collaborating across teams, communicating technical tradeoffs clearly, and learning from senior engineers on architecture and platform decisions.