Research Engineer, Orchestral

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. As a Research Engineer on the Orchestral team, you will help build the agentic platform we use to train and evaluate our models. Orchestral is the system our researchers rely on to run training and evaluation at scale — from the rollout engine that powers agentic workflows to the infrastructure that keeps their workloads running reliably. What you build becomes the foundation the whole science team works on. Your work will span the platform's core: the library, tooling, abstractions and rollout engine — including new capabilities like long-running tasks, subagents and RL features — and the machinery that runs data and jobs at scale, with a focus on resiliency, routing and request handling. You will own significant pieces end to end, from design to production. This is a software engineering role rather than an ML or infrastructure-operations one: RL or PyTorch experience is not required, and you will spend your time building the platform, not operating it. You will be building a distributed RL system, with the scientists as your users. What You Will Do Design and build the core of the Orchestral platform: the library, abstractions and tooling researchers use to train and evaluate models. Build and evolve the rollout engine, shipping new capabilities such as long-running tasks and subagents. Run Orchestral as a production system: keep training and evaluation workloads reliable at scale. Improve how data and jobs flow through the platform as usage grows — scheduling, routing and request handling. Treat researchers as your users: understand their workflows and turn recurring needs into robust, shared tooling. Raise the bar on engineering quality across the platform: testing, code review, CI/CD, observability and documentation. Contribute to open-source rollout-engine and agent-infrastructure projects where it benefits the platform. What We're Looking For Experience directly building and debugging complex distributed systems at scale, with an emphasis on reliability and scalability. Strong Python skills and solid software-design instincts: testing, code review, CI/CD. A builder mindset: you have constructed systems from the ground up, not just operated polished ones. A track record in small teams running complex systems at scale — for example in finance, crypto or ad tech. Self-starter, lo