Research Engineer, Forge

ph3About Mistral /h3 pMistral 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. /p h3Role Summary /h3 pAs 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. /p h3What You Will Do /h3 ul liBuild and improve post‑training and evaluation workflows (CPT/SFT/RL/distillation), turning prototypes into repeatable Forge “recipes”. /li liDevelop tools and pipelines for synthetic data generation, data curation, training, evaluation, and deployment. /li liDebug and harden large‑scale ML systems: distributed training, scheduling/execution, checkpointing, observability, and reproducibility. /li liImprove the Forge codebase via clear APIs, tests, documentation, and maintainable abstractions. /li liPush the frontier of our RL training stack (e.g., high-throughput async rollout and scalable post‑training systems at frontier-model scale). /li liMake sure Forge deployment is seamless and adaptable to a diversity of clients (hardware access, software stack, cloud and on‑premises, …). /li liPartner with researchers and infrastructure engineers to translate bottlenecks into concrete system improvements. /li /ul h3About You /h3 ul liStrong Python engineering skills and experience working in large codebases (testing, code review, CI, operational ownership). /li liHands‑on experience with PyTorch, JAX, or similar. /li liStrong systems and infrastructure fundamentals. /li liExperience with LLM training or post‑training: fine‑tuning, RL, distillation, evaluation, and/or data pipelines. /li liExcellent debugging skills in ambiguous systems (distributed jobs, data issues, quality regressions, infra failures). /li liClear communication with technical and non‑technical stakeholders. /li liHigh agency, low ego, and comfort in fast‑moving, under‑specified environments. /li /ul h3Nice to have /h3 ul liDistributed training experience (FSDP, DeepSpeed, Megatron, etc.). /li liCluster/orchestration experience (SLURM, Ray, Kubernetes, Kueue, Karpenter, Skypilot, etc.). /li liExperience building reliable ML infrastructure, evaluation systems, or large‑scale data processing pipelines. /li liResearch experience in LLMs, agents, multimodal models, reasoning, code, or domain adaptation. /li liOpen‑source contributions, publications, or widely used internal tooling. /li liExperience training multi‑billion‑parameter models (pre‑training or RL). Experience training on petabyte‑and exabyte‑scale datasets. /li liAbility to identify bottlenecks across the stack and drive improvements from first principles. /li /ul h3What we offer /h3 pWe 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. /p h3Privacy Policy /h3 pYour privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy. /p /p #J-18808-Ljbffr