Research Engineer, Forge

Mistral.ai; Paris; Sur place; EUR 120 000 - 170 000; Plein temps

Avantages offerts par ce poste

Healthcare coverage

Relocation support

Retirement plans

Wellness programs

Résumé du poste

Mistral is seeking a Research Engineer for Forge to translate real customer needs into reliable training and deployment workflows across CPT/SFT/RL/distillation pipelines. You will work end‑to‑end from model adaptation to production, balancing experimentation with scalable systems.

You will collaborate with researchers and infrastructure engineers to ship robust, maintainable Forge projects, ensuring compatibility across hardware, cloud, and on‑premises while driving improvements from first

Qualifications

• Strong Python engineering experience in large codebases.
• Experience with PyTorch, JAX, or similar.
• Hands-on experience with LLM training or post-training workflows.
• Excellent debugging in distributed systems and clear communication.

Responsabilités

• Build and improve post-training and evaluation workflows (CPT/SFT/RL/distillation), turning prototypes into repeatable Forge “recipes”.
• Develop tools and pipelines for synthetic data generation, data curation, training, evaluation, and deployment.
• Debug and harden large-scale ML systems: distributed training, scheduling/execution, checkpointing, observability, and reproducibility.
• Improve the Forge codebase via clear APIs, tests, documentation, and maintainable abstractions.
• Push the frontier of our RL training stack (high-throughput async rollout and scalable post-training systems).
• Make sure Forge deployment is seamless and adaptable to a diversity of clients (hardware, cloud, on-premises).
• Partner with researchers and infra engineers to translate bottlenecks into concrete system improvements.

Connaissances

Python

PyTorch

JAX

LLM training

Distributed systems

Debugging

Stakeholder communication

Infrastructure basics

Outils

Kubernetes

SLURM

Kueue

DeepSpeed

Description du poste

About Mistral

Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems—across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector—co-creating customized AI systems that they can run on their terms.

We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.

Role summary

As a Research Engineer on Forge, you will turn real customer requirements into reliable training and deployment workflows. You’ll work end‑to‑end across model adaptation and post‑training (CPT/SFT/RL/distillation), evaluation, data, and infrastructure. The role bridges research experimentation and production constraints.

This role sits in Applied Science, with direct impact on client outcomes. You’ll collaborate closely with scientists, engineers, product, and customer‑facing teams to ensure Forge projects ship, are maintainable, and can be trusted by others.

Interview focus can vary (algorithms, infrastructure, evals, or data). You don’t need to match every bullet below to apply.

What you will do

• Build and improve post‑training and evaluation workflows (CPT/SFT/RL/distillation), turning prototypes into repeatable Forge “recipes”.
• Develop tools and pipelines for synthetic data generation, data curation, training, evaluation, and deployment.
• Debug and harden large‑scale ML systems: distributed training, scheduling/execution, checkpointing, observability, and reproducibility.
• Improve the Forge codebase via clear APIs, tests, documentation, and maintainable abstractions.
• Push the frontier of our RL training stack (e.g., high-throughput async rollout and scalable post‑training systems at frontier-model scale)
• Make sure Forge deployment is seamless and adaptable to a diversity of clients (hardware access, software stack, cloud and on‑premises, …)
• Partner with researchers and infrastructure engineers to translate bottlenecks into concrete system improvements.

About you

• Strong Python engineering skills and experience working in large codebases (testing, code review, CI, operational ownership).
• Hands‑on experience with PyTorch, JAX, or similar.
• Strong systems and infrastructure fundamentals.
• Experience with LLM training or post‑training: fine‑tuning, RL, distillation, evaluation, and/or data pipelines.
• Excellent debugging skills in ambiguous systems (distributed jobs, data issues, quality regressions, infra failures).Clear communication with technical and non‑technical stakeholders.
• High agency, low ego, and comfort in fast‑moving, under‑specified environments.

Nice to have

• Distributed training experience (FSDP, DeepSpeed, Megatron, etc.).
• Cluster/orchestration experience (SLURM, Ray, Kubernetes, Kueue, Karpenter, Skypilot, etc.).
• Experience building reliable ML infrastructure, evaluation systems, or large‑scale data processing pipelines.
• Research experience in LLMs, agents, multimodal models, reasoning, code, or domain adaptation.
• Open‑source contributions, publications, or widely used internal tooling.
• Experience training multi‑billion‑parameter models (pre‑training or RL). Experience training on petabyte- and exabyte-scale datasets.
• Ability to identify bottlenecks across the stack and drive improvements from first principles.

What We Offer

We offer a comprehensive benefits package designed to support your well‑being, growth, and work‑life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location‑specific perks.

For the most up‑to‑date details on benefits available in your location, please refer to our Benefits page.

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