Mistral· Research· Palo Alto
Research Engineer, Machine Learning
Classified Tasks (13)
Automate 0%Augment 77%Human-Only 23%
Augment (10)
AI assists, human decides
Build and optimize large-scale learning systems that power open-weight models.
technical
Enhance the shared training framework, data pipelines, and cluster tooling used across teams.
operational
Implement and maintain robust large-scale ML pipelines and tooling to accelerate researchers.
operational
Integrate model checkpoints into training and deployment workflows.
technical
Streamline model evaluation pipelines and metrics collection.
analytical
Expose model functionality via APIs for research and production use.
technical
Conduct experiments on advanced deep-learning techniques, including sparsified models and large-scale distributed runs across GPU clusters.
analytical
Implement ML algorithms in clear, efficient Python code.
technical
Benchmark algorithms and systems at scale to evaluate performance and resource utilization.
analytical
Deliver prototypes and production-grade components for Le Chat and the enterprise API.
technical
Human-Only (3)
Requires human judgment
Collaborate with Research Scientists to translate research ideas into repeatable, scalable production code.
communication
Embed within research squads to turn fresh ideas (e.g., Alignment, Pre‑training, Multimodal) into deployable implementations.
communication
Design ML algorithms and architectures for large-scale training.
creative
Job description
About Mistral At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life. We democratize AI through high-performance, optimized, open-source and cutting-edge models, products and solutions. Our comprehensive AI platform is designed to meet enterprise as well as personal needs. Our offerings include Le Chat, La Plateforme, Mistral Code and Mistral Compute - a suite that brings frontier intelligence to end-users. 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 France, USA, UK, Germany and Singapore. We are creative, low-ego and team-spirited. Join us to be part of a pioneering company shaping the future of AI. Together, we can make a meaningful impact. See more about our culture on https://mistral.ai/careers. 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.