ModelOp's Enterprise AI Command Center is the system of record that unifies every AI asset so you bring ML, GenAI, and agentic AI to production 10× faster. Backed by a16z.
About the role
We are looking for a Large-Scale Mid-Training/RL Infrastructure Engineer to help build, optimize, and scale out our in-house foundation model training stack, with an emphasis on both core pretraining and RL-enhanced methods. This role is deeply technical and directly impacts Peano AI's core model product.
What they're looking for
- 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
More about this role
Peano AI is building the infrastructure and application stack for the next generation of agentic AI systems.
We believe token usage will grow exponentially over the coming years, but routing all inference and training through closed model providers will remain too expensive for many users and enterprises. Our thesis is that agentic applications require a vertically integrated stack: high-throughput, cost-efficient serving and training infrastructure paired with an application layer designed for long-running, agentic workloads.
Peano AI is building the Agent Cloud, a serving and training infrastructure platform purpose-built for agentic workloads, long-context inference, large-scale open-source model deployment, and pretraining of our own foundation model. By combining infrastructure and application design, we aim to make open-source models and custom foundation models significantly more performant, practical, and competitive.
We are looking for a Large-Scale Mid-Training/RL Infrastructure Engineer to help build, optimize, and scale out our in-house foundation model training stack, with an emphasis on both core pretraining and RL-enhanced methods. This role is deeply technical and...
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