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Research Engineer - Reinforcement Learning
San Francisco, CA (In-Person)
General Diffusion’s mission is to decouple intelligence from silicon. We believe the path to AGI requires a universal translation layer that makes compute fungible across any architecture.
About the role
As a Research Engineer in Reinforcement Learning, you will apply RL techniques to the problem of compute scheduling. You will train agents that learn to predict optimal graph partitions and resource allocations in real-time, turning an NP-hard combinatorial optimization problem into an inference task.
What you might work on
- Training RL agents to optimize compute graph partitioning for heterogeneous clusters.
- Developing simulation environments that accurately model network latency and bandwidth constraints.
- Implementing "predictive auto-scaling" that anticipates workload spikes before they happen.
- Collaborating with the RS1 team to deploy RL-based schedulers into production.
What we’re looking for
- Strong background in Deep Reinforcement Learning (PPO, SAC, MCTS).
- Experience applying ML to systems problems (ML for Systems).
- Proficiency in PyTorch or JAX.
- Ability to design and implement custom gym environments.
Our culture
- Silicon Neutrality. We build for the world where compute is a commodity.
- Radical Efficiency. We believe software bloat is an existential risk.
- Deep Work. We value long periods of uninterrupted focus.