About the job

At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology integrates 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 meets enterprise needs, whether on-premises or in cloud environments, including offerings like le Chat, the AI assistant for life and work. 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, with teams distributed across France, USA, UK, Germany, and Singapore. As a Research Engineer on the software side, you will design and harden the codebase, tools, and distributed services that let scientists train and ship frontier-scale models. You do not need prior ML experience; what matters is writing clean, reliable, scalable code. You will join our Platform REs team to build and maintain shared dev-tools, evaluation and data pipelines, training frameworks, cluster tooling, and CI/CD. Your role includes accelerating researchers by owning complex parts of large-scale pipelines, delivering robust internal tooling, interfacing research with product by exposing clean APIs, automating model pushes, and surfacing live metrics. You will write efficient, well-tested Python and systems code, enforce code reviews, CI, and observability, design and optimize distributed services like Kubernetes and SLURM for thousands-of-GPU jobs, and prototype utilities carried through to stable shared libraries. The team moves fluidly along the research-to-production spectrum, and engineers can rotate between Platform and Embedded tracks as interests evolve. The role requires a Master’s in Computer Science (or equivalent experience), 4+ years building and operating large-scale or distributed systems, strong software design instincts, fluency in Python and at least one systems language, hands-on experience with container orchestration and schedulers, and comfort profiling performance and automating workflows. Nice-to-haves include exposure to ML workloads, GPU clusters, and open-source contributions. Benefits include competitive cash salary and equity, health insurance, parental leave, visa sponsorship, and UK-specific perks like transport reimbursement and gym membership allowance.