Senior Machine Learning Engineer, ML Platform
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Before you apply
Salary€50k – €80k
LocationParis, IDF, FR
Work modehybrid
Typefull-time
SenioritySenior
Experience5+ yrs
DepartmentR&D
Company size201–500 people
First seenOct 16, 2026 · 3w ago
Verified live17h ago
At a glanceSummarised by Seekless from the posting.
Master's degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience
5+ years of software engineering experience, including a significant portion building infrastructure or tooling used by machine learning teams, such as an ML platform or a training framework
Strong Python skills and software engineering fundamentals: API design, testing, refactoring, and attention to developer experience
Solid PyTorch knowledge and practical experience with model training and profiling
Experience running ML workloads on a cloud platform, ideally GCP (Vertex AI, Artifact Registry, IAM, storage), and with containerization
Experience taking models to production: model packaging, inference serving, and performance and reliability constraints
Ability to work closely with researchers, translate recurring pain points into shared tooling, and push back constructively when needed
Ability to work autonomously in a distributed, multi-site team, with clear verbal and written communication in English
Medical imaging experience (DICOM, 3D volumes, large images)
HPC and Slurm experience
Contributions to open-source ML tooling
Skills
Python
PyTorch
GCP
Vertex AI
Artifact Registry
IAM
Slurm
DICOM
About the role
The Senior Machine Learning Engineer, ML Platform is responsible for building, running, and evolving the ML monorepo used by the X-ray, CT, and MRI machine learning group at DeepHealth. The monorepo covers the full path from experimentation to product: dataset and sample definitions, training and evaluation code, experiment tracking, model export, and the inference servers that run in production. Everyone on the team contributes to the monorepo; this role is the single point of accountability for it. This hands-on role owns the architecture, tooling, and engineering standards of that codebase for a distributed team of about twenty machine learning engineers and scientists, working with model developers and software engineers to make the team’s work fast, better, and easier to audit. It also leads the team’s agentic coding strategy, shaping how AI coding agents are used across the codebase. The role reports to the Leader of the ML for X-ray/CT/MRI group.
Essential Duties and Responsibilities
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Own the architecture and roadmap of the ML Monorepo:
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Module boundaries, shared abstractions for datasets and samples, training and evaluation loops, configuration, and experiment tracking.
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Build and run the engineering tooling around the codebase:
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Continuous integration, testing, dependency management, containers, artifact and model registries, and GPU job scheduling on cloud (GCP) and HPC infrastructure.
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Own the path from trained checkpoint to deployable model:
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Model packaging, inference servers with enforced API contracts, reproducibility, and performance and memory budgets on target hardware.
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Define and enforce engineering standards (code review, testing, documentation) in a way that accelerates research rather than slowing it down.
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Ensure traceability of data, code, and model versions across experiments and releases.
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Investigate and resolve performance, reliability, and usability issues in the stack raised by the team and reduce their recurrence through shared tooling.
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Drive the team’s agentic coding strategy: define how AI coding agents are used in the monorepo, set up the tooling, guardrails, and conventions that make them effective, and help colleagues adopt them.
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Review code, mentor colleagues on engineering practices, and communicate technical decisions and trade-offs clearly.
PLEASE NOTE: This is not an exhaustive list of all duties, responsibilities and requirements of the position described above. Other functions may be assigned and management retains the right to add or change duties at any time.
Minimum Qualifications, Education and Experience
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Master’s degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience (required).
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5+ years of software engineering experience, including a significant portion building infrastructure or tooling used by machine learning teams, such as an ML platform or a training framework (required).
•
Strong Python skills and software engineering fundamentals: API design, testing, refactoring, and attention to developer experience (required).
•
Solid PyTorch knowledge and practical experience with model training and profiling (required).
•
Experience running ML workloads on a cloud platform, ideally GCP (Vertex AI, Artifact Registry, IAM, storage), and with containerization (required).
•
Experience taking models to production: model packaging, inference serving, and performance and reliability constraints (required).
•
Ability to work closely with researchers, translate recurring pain points into shared tooling, and push back constructively when needed (required).
•
Ability to work autonomously in a distributed, multi-site team, with clear verbal and written communication in English (required).
•
Medical imaging experience (DICOM, 3D volumes, large images) (preferred).
Nice to have
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HPC and Slurm experience (preferred).
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Contributions to open-source ML tooling (preferred).
Quality Standards
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Communicates, cooperates, and consistently functions professionally and harmoniously with all levels of supervision, co-workers, visitors, and vendors.
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Demonstrates initiative, personal awareness, professionalism and integrity, and exercises confidentiality in all areas of performance.
Legal
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Follows all local, regional and country laws concerning employment.
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Follows all DeepHealth policies and procedures.
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Follows data privacy, compliance, safety and confidentiality standards at all times.
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Practices universal safety precautions.
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Promotes good public relations on the phone and in person.
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Adapts and is willing to learn new tasks, methods, and systems.
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Reports to work regularly as scheduled; consistently punctual with respect to working hours, meal and rest breaks, and maintains satisfactory personal attendance in accordance with DeepHealth guidelines.
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Completes job responsibilities in a quality and timely manner.
Travel
This position may require occasional travel between team sites (Paris, the Netherlands, Grenoble/Rennes).
Working Environment
Office / Hybrid. Paris, Île-de-France, France.
Physical Demands
The employee must be able to perform the essential duties and responsibilities of the position, with or without reasonable accommodation.
Salary:
Dependent upon experience
50,000-80,000 EURO