Research Engineer, Forge

Mistral AI is hiring a Research Engineer, Forge for a remote role open to applicants in France. Level rates it ; you can apply on Mistral AI.

Level's read on this job

On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
The posting lists this role as remote.
Tools and skills the posting cites include PyTorch, JAX and machine learning.
Level classifies it as a senior role.
Level lists 234 other open roles at Mistral AI.

AI in this role

Build and improve post-training, evaluation, and deployment workflows for machine learning models on Forge.

PyTorchJAX

machine-learningdistributed-trainingmlopspython

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.

Privacy Policy

Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy.

How we rate this

Research Engineer, Forge at Mistral AI rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Research Engineer, Forge is an open job at Mistral AI.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Paris and is marked remote.
The job was posted on September 4, 2026.
Seniority: senior.
Applications are handled by Mistral AI. Apply on Mistral AI

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Machine learningDistributed TrainingMLOpsPythonPyTorchJAX

Questions you could be asked

Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where distributed training was part of your work. What did you do?
Tell me about a project where mlops was part of your work. What did you do?
Tell me about a project where python was part of your work. What did you do?
Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Machine learning, Distributed Training, MLOps, Python, and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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