Senior ML Platform Engineer — Scalable Production ML

DeepHealth; Paris; Hybrid; EUR 50,000 - 80,000; Full time

Job summary

DeepHealth is seeking a Senior Machine Learning Engineer to own the ML Monorepo for X-ray, CT, and MRI teams. You will define architecture, tooling, and standards, and collaborate with researchers and software engineers to move models from experiment to production.

The role includes leading CI, model packaging, GPU scheduling on cloud (GCP) and HPC, and ensuring traceability across experiments. Strong Python, PyTorch, and English communication are required.

Qualifications

• Master's degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience.
• 5+ years of software engineering experience building infrastructure or tooling for ML teams.
• Strong Python skills and software engineering fundamentals (API design, testing, refactoring).
• Solid PyTorch knowledge with model training and profiling experience.
• Experience running ML workloads on cloud platforms (GCP preferred) and with containerization.

Responsibilities

• Own the architecture and roadmap of the ML Monorepo, including modules and experiment tracking.
• Build and run tooling for CI, testing, dependencies, containers, and model registries.
• Own the path from trained checkpoint to deployable model with packaging and inference servers.
• Define engineering standards to accelerate research without slowing it down.
• Ensure traceability of data, code, and model versions across experiments.
• Mentor colleagues on engineering practices and communicate trade-offs clearly.

Skills

Python

English communication

Collaboration with researchers

Autonomous distributed teamwork

System architecture

Education

Master's degree in computer science, engineering, mathematics, or related field

Tools

GCP (Vertex AI)

Artifact Registry

IAM

Docker / Containerization

Slurm HPC

Job description

DeepHealth is seeking a Senior Machine Learning Engineer to own the ML Monorepo for X-ray, CT, and MRI teams. You will define architecture, tooling, and standards, and collaborate with researchers and software engineers to move models from experiment to production.

The role includes leading CI, model packaging, GPU scheduling on cloud (GCP) and HPC, and ensuring traceability across experiments. Strong Python, PyTorch, and English communication are required.