AI role · Open offer
Senior ML Engineer — ad ranking
Criteo
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.
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At a glance
How you'll work, where, and key listing dates.
- Employment
- Permanent (CDI), Full-time
- Workplace
- Hybrid
- Location
- 32 Rue Pasteur, 69007 Lyon, France · Hybrid
- Compensation
- EUR 65k–90k/year + BSPCE
- Experience level
- Senior
- Team
- Commerce ML
- Benefits & perks
About this role
What the team is looking for and how this position fits.
Criteo is hiring a Senior Senior ML Engineer — ad ranking on the Commerce ML team (Hybrid · Lyon, France). This is a hands-on role in search & recommendations—not a generic “AI enthusiast” post.
You will own ranking, retrieval, and online experimentation 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.
**Criteo** powers **commerce media** and retail advertising — real-time bidding and personalization from **Lyon** engineering hub.
Senior ML Engineer (hybrid Lyon 7e): **conversion models**, **creative optimization**, and **auction intelligence**.
About Criteo
- Global ad-tech leader with strong French engineering roots.
- Lyon office hosts ML teams serving retailer partnerships.
- Billions of daily predictions with strict latency budgets.
What you will do
- Improve CTR/CVR models with causal and uplift methods.
- Optimize feature pipelines for sub-10ms scoring.
- Run holdout experiments tied to advertiser ROI.
Stack & environment
- Python, PyTorch, Spark, Kafka, Kubernetes.
- Hybrid Lyon; French and English.
What we look for
- 5+ years ML in ads, recommendations, or marketplaces.
- Eligible to work in France.
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
Tools & stack
Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).
- KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
- KafkaTool
Streaming features or scores with ordering, idempotency, and monitoring for real-time systems.
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: Real-time bidding, conversion models, or creative optimization.
- ✓Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, Spark, and Kubernetes.
- ✓You have owned ranking, retrieval, and online experimentation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
- ✓You are comfortable with the hybrid rhythm (Hybrid · Lyon, 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.
- ✓
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What the employer asked for
Real-time bidding, conversion models, or creative optimization.
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