Senior Software Engineer, RL Post-Training Frameworks
Tasks
- Architect RL post training infrastructure
- Build scalable distributed systems
- Collaborate with networking math library and compiler teams
- Design end-to-end distributed systems
- Develop fault tolerance and elastic scaling
- Enable fast restarts for distributed training
- Improve open source RL framework performance
- Integrate CPU rollout tool use and code execution
- Partner with framework maintainers to improve runtimes
- Tune RL training inference rollout loops
Perks/Benefits
Skills/Tech-stack
C# | C++ | CPUs | CUDA | Container lifecycle | Distributed Systems | Elastic scaling | FSDP | Fault Tolerance | GPU Allocation | GPUs | High Performance | High-Performance Computing | Infiniband | Kubernetes | Kubernetes Resource Quotas | LPUs | Machine Learning | Mixed Precision | NCCL | NVLink | Performance Computing | Pipeline parallelism | Pod Scheduling | PyTorch | Python | Ray | Reinforcement Learning | Resource Quotas | SGLang | Tensor Parallelism | TensorRT-LLM | VLLM
Education
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