Senior Machine Learning Engineer, ML Platform

About the role

The Senior Machine Learning Engineer, ML Platform will own and evolve the ML monorepo that supports DeepHealth's X‑ray, CT and MRI teams. You will ensure the codebase, tooling and standards enable fast, reliable research and production deployment for a distributed team of around twenty ML engineers and scientists.

Key responsibilities

• Define the architecture and roadmap of the ML monorepo, including module boundaries, dataset abstractions, training loops, configuration and experiment tracking.
• Build and maintain CI/CD pipelines, testing frameworks, dependency management, container images, artifact and model registries, and GPU job scheduling on GCP and HPC clusters.
• Own the path from trained checkpoint to deployable model, handling packaging, inference server APIs, reproducibility and performance budgets on target hardware.
• Establish and enforce engineering standards (code review, testing, documentation) that accelerate research.
• Ensure traceability of data, code and model versions across experiments and releases.
• Lead the team’s agentic coding strategy, defining tooling, guardrails and conventions for AI coding agents.

Required profile

• Master’s degree in Computer Science, Engineering, Mathematics or related field, or equivalent practical experience.
• 5+ years of software engineering experience, with a strong focus on ML infrastructure or tooling.
• Proven ability to work autonomously in a distributed, multi‑site team and communicate clearly in English.

Required skills

• Python programming and software engineering fundamentals (API design, testing, refactoring).
• PyTorch for model training and profiling.
• Google Cloud Platform services (Vertex AI, Artifact Registry, IAM, storage) and containerization.
• Experience with model packaging, inference serving and performance constraints.
• Medical imaging knowledge (DICOM, 3D volumes) – preferred.
• HPC and Slurm – preferred.
• Contributions to open‑source ML tooling – preferred.