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ML Engineer — federated learning | Owkin | Paris | Ganloss

AI role · Open offer

ML Engineer — federated learning

Owkin

Permanent (CDI)
Full-time
Senior
Biomedical ML
Hybrid
74 Rue du Faubourg Saint-Antoine, Paris, IDF 75012, France · Hybrid
Paris, France
EUR 82k-110k/year + BSPCE

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

3 skills1 tool

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At a glance

How you'll work, where, and key listing dates.

Employment: Permanent (CDI), Full-time
Workplace: Hybrid
Location: 74 Rue du Faubourg Saint-Antoine, Paris, IDF 75012, France · Hybrid
Compensation: EUR 82k-110k/year + BSPCE
Experience level: Senior
Team: Biomedical ML
Benefits & perks: Mission recherche, télétravail partiel.
How to apply: Projet fédéré ou privacy-preserving ML.
Company site: owkin.com
Listed: Jul 1, 2026
Updated: Aug 3, 2026

About this role

What the team is looking for and how this position fits.

Owkin is hiring a Senior ML Engineer — federated learning on the Biomedical ML team (Hybrid · Paris, France). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Owkin fait de la recherche biomédicale avec apprentissage fédéré et partenaires hospitaliers.

ML Engineer — federated learning (Paris) : privacy-preserving training, évals cliniques, et MLOps réglementé.

What you will do

Ship production federated healthcare ML with clear evaluation, monitoring, and rollback paths—not demo-only notebooks.
Partner with product, design, and domain experts on requirements, trade-offs, and safe rollout.
Improve data quality, labeling, and offline metrics that correlate with business KPIs.
Document architectures and runbooks for on-call and customer-facing teams.
Mentor engineers on testing, observability, and responsible AI practices.

What we look for

Strong Python and modern ML/LLM stack experience with multiple shipped features.
Comfort with SQL, experimentation, and communicating results to non-technical stakeholders.
Pragmatic ownership from prototype to production—including cost and latency budgets.
Eligible to work in the listed country; professional English (French a plus for Paris roles).

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
SQL
Analytical SQL for features, cohorts, label quality, and experiment analysis at scale.

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Projet fédéré ou privacy-preserving ML.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, and Docker.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Paris, France)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: EUR 82k-110k/year + BSPCE, benefits in At a glance) and your expectations align.

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What the employer asked for

Projet fédéré ou privacy-preserving ML.

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ML Engineer — federated learning

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