Founding Mid-Training/RL Infrastructure Engineer

Peano AI is building the Agent Cloud, a serving and training infrastructure platform for agentic AI workloads, long-context inference, large-scale open-source model deployment, and foundation model pretraining. The Large-Scale Mid-Training/RL Infrastructure Engineer will architect, optimize, and scale distributed infrastructure for foundation model pretraining and reinforcement learning across massive GPU and TPU clusters, while integrating research capabilities into production-ready systems.

Responsibilities:

• 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
• 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 at unmatched scale
• 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
• Tune memory usage, mixed-precision, and runtime performance for massive models and long-context workloads
• Debug and profile training performance bottlenecks—across model code, distributed compute, networking, and infrastructure
• Collaborate with application, infrastructure, and research teams to ensure our foundation models deliver on performance and functionality
• Translate research prototypes and experimental features into production-ready, scalable training systems that operate across state-of-the-art compute clusters