AI role · Open offer

Senior NLP Engineer — translation & LLMs

DeepL

Permanent (CDI)
Full-time
Senior
Language models
Hybrid
Paris / Cologne · Hybrid · EU
Paris, France
EUR 70k–95k/year + equity

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.

4 skills2 tools

At a glance

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

Employment: Permanent (CDI), Full-time
Workplace: Hybrid
Location: Paris / Cologne · Hybrid · EU
Compensation: EUR 70k–95k/year + equity
Experience level: Senior
Team: Language models
Benefits & perks

About this role

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

DeepL is hiring a Senior Senior NLP Engineer — translation & LLMs on the Language models 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.

DeepL est leader de la traduction neuronale — équipes EU (Paris / Cologne).

Senior NLP Engineer: modèles de traduction et LLMs multilingues avec evals qualité.

Missions

Améliorer qualité MT / LLM sur paires de langues prioritaires.
Concevoir des benchmarks humains et automatiques.

Stack & environment

Python, PyTorch, CUDA, Docker.

What we look for

NLP / MT en prod; allemand ou français un plus.

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.
NLP

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.
CUDATool
Regular use of CUDA in the LLM & generative AI stack—configuration, debugging, and team conventions.

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: MT, LLM translation, or multilingual eval you owned.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, NLP, Machine Learning, 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 70k–95k/year + equity, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

MT, LLM translation, or multilingual eval you owned.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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