Founding Mid-Training/RL Infrastructure Engineer

• 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.

You will architect, scale, and tune the distributed and accelerator-aware training infrastructure for massive language models, including data pipelines, model sharding, checkpointing, rollout and reward-based RL, and overall system throughput. Experience with large-scale model pretraining and mid-training interventions is required.

Preferred experience with frameworks such as Megatron, Transformer-Engine, verl, slime, and related large-scale training and RL toolkits. Familiarity with training pipeline scaling, mixed-precision, memory optimization, and deep understanding of both supervised and RL-based training cycles is essential., * Significant experience with large-scale deep learning model training and distributed system design, including large GPU/TPU clusters (GB300/VR200, TPU v7x or similar).

• Proven track record of pretraining and/or RL-based fine-tuning of large models (LLMs or comparable scale).
• Deep familiarity and hands-on experience with frameworks such as Megatron, Transformer-Engine, verl, slime, or similar large-scale/foundation-model toolkits.
• Experience with data pipeline design, model/data/optimizer sharding, checkpointing, rollout/reward design, and training operations at scale.
• Strong accelerator (NVIDIA GB300/VR200 GPUs, TPU v7x) and memory optimization skills, including at cluster scale.
• Excellent debugging, profiling, and performance-tuning abilities in distributed environments.
• Comfort working in deeply technical, high-ownership, early-stage startup settings.

Cultural Fit

• Hands-on technical excellence and strong engineering judgment
• End-to-end ownership-from design to implementation to production
• Bias for action: ship quickly, learn from failures, iterate
• High intensity during critical milestones, focused on customer and product outcomes
• Ability to work deeply and sustain high execution
• Clear communicator, low ego, strong collaborator
• Thrives in ambiguity, rapid change, and taking on multiple roles
• Lifelong learner with a belief that capability compounds with time and effort

If you are excited to build the core training and RL infrastructure for Peano AI’s vertically integrated foundation model-leveraging thousands of NVIDIA GB300/VR200 GPUs and TPU v7x accelerators, tackling the hardest scaling and optimization challenges, and helping push the boundaries of agentic AI-we’d love to talk.