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Mistral AIFast-track your ML job hunt :
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Mistral AI · Palo Alto · Hybrid
Research Engineer, Machine Learning
1/27/2026
Description
Role Summary
About the Research Engineering team
The team spans Platform (shared infra & clean code) and Embedded (inside research squads). Engineers can move along the research↔production spectrum as needs or interests evolve.
As a Research Engineer – ML track, you’ll build and optimise the large-scale learning systems that power our open-weight models. Working hand-in-hand with Research Scientists, you’ll either join:
- Platform RE Team: Enhance the shared training framework, data pipelines and cluster tooling used by every team; or
- Embedded RE Team: Sit inside a research squad (Alignment, Pre-training, Multimodal, …) and turn fresh ideas into repeatable, scalable code.
What will you do
- Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools.
- Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.
- Conduct experiments on the latest deep-learning techniques (sparsified 70 B + runs, distributed training on thousands of GPUs).
- Design, implement and benchmark ML algorithms; write clear, efficient code in Python.
- Deliver prototypes that become production-grade components for Le Chat and our enterprise API.
About you
- Master’s or PhD in Computer Science (or equivalent proven track record).
- 4 + years working on large-scale ML codebases.
- Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s).
- Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops.
- Strong software-design instincts: testing, code review, CI/CD.
- Self-starter, low-ego, collaborative.
- 💰 Competitive salary and equity.
- 🚑 Healthcare: Medical/Dental/Vision covered for you and your family.
- 👴🏻 Pension : 401K (6% matching)
- 🏝️ PTO : 18 days
- 🚗 Transportation: Reimburse office parking charges, or $120/month for public transport
- 🏀 Sport: $120/month reimbursement for gym membership
- 🥕 Meal stipend: $400 monthly allowance for meals (solution might evolve as we grow bigger)
- 🌎 Visa sponsorship
- 🤝 Coaching: we offer BetterUp coaching on a voluntary basis
By applying, you agree to our Applicant Privacy Policy.
Qualifications
- Platform RE Team: Enhance the shared training framework, data pipelines and cluster tooling used by every team; or
- Embedded RE Team: Sit inside a research squad (Alignment, Pre-training, Multimodal, …) and turn fresh ideas into repeatable, scalable code.
- Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools.
- Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.
- Conduct experiments on the latest deep-learning techniques (sparsified 70 B + runs, distributed training on thousands of GPUs).
- Design, implement and benchmark ML algorithms; write clear, efficient code in Python.
- Deliver prototypes that become production-grade components for Le Chat and our enterprise API.
- Master’s or PhD in Computer Science (or equivalent proven track record).
- 4 + years working on large-scale ML codebases.
- Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s).
Nice to have
- Strong software-design instincts: testing, code review, CI/CD.
- Self-starter, low-ego, collaborative.
Fast-track your ML job hunt :
Be the first to hear about new sota jobs + exclusive salary research + career cheatsheets.