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, 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-haves
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.