Research Engineer, Forge

Mistral; Lausanne; On-site; CHF 120,000 - 180,000; Full time

Job summary

Mistral is seeking an experienced Research Engineer to build end-to-end training and deployment workflows for Forge. You will bridge research experiments with production constraints, collaborating with researchers and infrastructure teams to ship reliable solutions.

The role emphasizes strong Python software engineering, PyTorch/JAX experience, and deep systems knowledge to support post-training workflows and scalable pipelines for client projects.

Qualifications

• Strong Python engineering skills in large codebases.
• Hands-on experience with PyTorch or JAX.
• Strong systems and infrastructure fundamentals.
• Experience with LLM training or post-training workflows.
• Excellent debugging skills in distributed environments.
• Clear communication with technical and non‑technical stakeholders.

Responsibilities

• Build and improve post-training and evaluation workflows.
• Develop tools and pipelines for data generation, training, evaluation, and deployment.
• Harden large-scale ML systems: distributed training, scheduling, observability.
• Improve codebase with APIs, tests, and maintainable abstractions.
• Advance RL training stack for frontier-model scale.
• Ensure Forge deployment is adaptable across diverse clients.

Skills

Python

Codebase ownership

PyTorch/JAX

Systems & Infra

LLM training

Debugging

Communication

Autonomy

Tools

PyTorch

JAX

SLURM

Kubernetes

Ray

Job description

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.

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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