Research Engineer, Orchestral

• 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.

• 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, low-ego and collaborative, with a product mindset toward your users.

• Curiosity about ML and RL — this experience is not required, but a desire to learn is.

Nice to have

• Hands-on experience with Ray or similar distributed-computing frameworks.

• Open-source contributions, especially to rollout-engine or agent-infrastructure projects such as SLIME, verl or PrimeRL.

• Experience building or working with agentic workflows and environments.