- Palo Alto, California, United States
- On-site
- Full-time
- Competitive Salary + Equity
Build, optimize, and scale the distributed training infrastructure for Peano AI’s foundation models, leveraging thousands of NVIDIA GB300/VR200 GPUs and TPU v7x accelerators for high throughput and efficiency.
Responsibilities
- Build, optimize, and scale the distributed training infrastructure
- Own and improve throughput, efficiency, scalability, cost, and reliability of end-to-end pretraining and RL pipelines
- Architect distributed data loading, sharding, checkpointing, batching, and accelerator utilization for multi-node LLM training
- Integrate and optimize RL components: rollout, reward modeling, environment orchestration, and mid-training signal injections
- Work with and extend frameworks such as Megatron, Transformer-Engine, verl, slime, and other high-performance training libraries
Requirements
- Nice to have: experience with large-scale/foundation-model toolkits
- Nice to have: familiarity with training pipeline scaling
- Nice to have: mixed-precision
- Nice to have: memory optimization
- Nice to have: deep understanding of supervised and RL-based training cycles