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Mistral is building full-stack AI solutions spanning frontier models, developer tools, applications, and compute infrastructure. The company partners with enterprises across finance, manufacturing, defense, healthcare, and the public sector to co-create customized AI systems.
As a Research Engineer on the Orchestral team, you will help build the agentic platform that powers Mistral's model training and evaluation at scale. Orchestral is the foundational system researchers depend on to run complex workloads reliably. You will own significant pieces of the platform end-to-end, from design through production.
Your responsibilities will include:
- Designing and building the core platform: the library, abstractions, and tooling researchers use to train and evaluate models
- Building and evolving the rollout engine, shipping new capabilities such as long-running tasks and subagents
- Running Orchestral as a production system, ensuring training and evaluation workloads remain reliable at scale
- Improving how data and jobs flow through the platform as usage grows—focusing on scheduling, routing, and request handling
- Treating researchers as your primary users: understanding their workflows and turning recurring needs into robust, shared tooling
- Raising engineering quality across the platform through testing, code review, CI/CD, observability, and documentation
- Contributing to open-source rollout-engine and agent-infrastructure projects where beneficial
This is a software engineering role focused on platform building rather than ML research or infrastructure operations. You will spend your time constructing the distributed system that scientists rely on, not operating it or conducting ML research.
REQUIREMENTS:
- Direct experience building and debugging complex distributed systems at scale, with emphasis on reliability and scalability
- Strong Python skills and solid software-design instincts (testing, code review, CI/CD)
- Builder mindset: you have constructed systems from the ground up, not just operated polished ones
- Track record in small teams running complex systems at scale (e.g., finance, crypto, ad tech)
- Self-starter, low-ego, collaborative, with a product mindset toward your users
- Curiosity about ML and RL (experience not required, but desire to learn is essential)
NICE-TO-HAVES:
- Hands-on experience with Ray or similar distributed-computing frameworks
- Open-source contributions to rollout-engine or agent-infrastructure projects (SLIME, verl, PrimeRL)
- Experience building or working with agentic workflows and environments
