Building inference systems that power cutting-edge RL research, the full-time Research Engineer - Inference will optimize GPU inference systems and improve rollout throughput for large-scale models, with the flexibility to work remotely or onsite in San Francisco.
Key responsibilities:
Extend vLLM integration for new model architectures and validate on small scales before larger runs
Optimize rollout throughput for long-horizon agents by tuning concurrency and KV-cache reuse
Collaborate with model researchers to enhance inference efficiency while balancing model quality and execution cost
Required qualifications:
Experience building or optimizing GPU inference systems and understanding inference-engine internals
Strong Python skills with experience in PyTorch or a comparable ML framework
Ability to profile end-to-end workloads and implement verified performance improvements
Experience debugging numerical or integration issues in ML systems