Research Engineer, Forge

Mistral.ai · Parismid

En corto

▸Ingeniero de investigación que transforma necesidades de clientes en flujos de entrenamiento y despliegue confiables.
▸Trabajas en todo el ciclo: post-entrenamiento, evaluación, datos, infraestructura y código, conectando investigación con producción.
▸Destacado: impulsas el avance de sistemas de entrenamiento por refuerzo a escala de modelos de vanguardia.

Proficiency in English is required for collaboration across global teams.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

🎧¿Llegás a la entrevista? Llevá el copiloto. Nuestra extensión escucha la entrevista en vivo y te muestra anclas de 3-4 palabras desde tu CV y tu preparación — mirás, conectás, hablás. Gratis. Ver la extensión →

¿Qué piden?

✓Excelentes habilidades en Python y experiencia en grandes bases de código.
✓Experiencia práctica con PyTorch, JAX o similares.
✓Conocimiento en entrenamiento o post-entrenamiento de LLMs (fine-tuning, RL, distillation, evaluación).
✓Habilidades sólidas para depuración en sistemas distribuidos y complejos.
✓Capacidad para comunicarse con equipos técnicos y no técnicos.
✓Agencia alta, bajo ego y comodidad en entornos rápidos y poco definidos.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PyTorchJAXFSDPDeepSpeedMegatronSLURMRayKubernetesKueueKarpenter

¿A quién escribirle en Mistral.ai?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

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. , 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. ). ). 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. 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 . P

No apliques sin prepararte

Investigamos quién te entrevista, adaptamos tu CV y te ensayamos en vivo — gratis la primera.