Member of Technical Staff — Post-Training Infrastructure
Embedding Vc · San Francisco Bay Area
About this role
## About Moonlake Moonlake is building the frontier of interactive world models—systems that generate, simulate, and reason over 3D environments for robotics, embodied AI, and interactive applications. We develop the infrastructure that enables intelligent systems to learn, evaluate, and interact within realistic virtual environments before operating in the physical world. Our work sits at the intersection of: - Robotics - Embodied AI - Interactive 3D Worlds - World Models - Simulation Infrastructure - Physical AI Moonlake has raised **$50M** in seed funding from **NVIDIA Ventures, Threshold Ventures, AIX Ventures**, and notable angels including **Naval Ravikant** and **Jeff Dean**. --- ## The Role Moonlake is hiring a **Member of Technical Staff** to lead **reinforcement learning infrastructure** and **model post-training**. You’ll work closely with Qi and the research team to improve large **vision-language** and **code-generating** agents through **fine-tuning, reinforcement learning, trajectory data, and scalable evaluation**. We’re looking for a full-stack researcher and engineer who understands the complete training system and can make strong judgments about: - when training is necessary - which methods are likely to work - what not to pursue --- ## What You’ll Do - Build **RL and post-training pipelines** for multimodal, vision-language, and code-generating agents - Develop infrastructure for **supervised fine-tuning**, **preference optimization**, **reward modeling**, and **reinforcement learning** - Create systems for **collecting, filtering, replaying, and learning from agent trajectories** - Design **rewards, verifiers, and evaluations** for long-horizon agent tasks - Improve agents’ ability to **plan, write and execute code, use tools, recover from errors, and complete complex workflows** - Scale **distributed training** and **high-throughput rollout generation** across **multi-GPU** environments - Improve **training reliability, reproducibility, observability, and cost efficiency** - Help define Moonlake’s long-term strategy for agent, robotics, and embodied-model training --- ## What We’re Looking For - Real-world experience training large **language**, **vision-language**, **multimodal**, or **code** models - Strong experience in **reinforcement learning**, **post-training**, or **large-scale fine-tuning** - Experience building **distributed training** or **high-throughput inference** systems - Familiarity with **supervised fine-tuning**, **preference optimization**, **reward modeling**, and **agentic RL** - Experience with **code-generation agents**, **long-horizon evaluation**, or **tool-using systems** - Strong **Python** skills and experience with **PyTorch**, **JAX**, or similar frameworks - Ability to work across **data, models, environments, rewards, evaluation, and infrastructure** - Strong research judgment and a bias toward building **reliable systems** --- ## Preferred Experience - Experience at a frontier AI lab or organization operating large-scale training systems - Experience with **code-model post-training** or **autonomous coding agents** - Experience with **multimodal models**, **robotics**, **simulation**, or **embodied AI** - Experience designing **verifiable rewards** or **outcome-based training systems** - Experience scaling **RL workloads** across large GPU clusters --- ## Success Looks Like (First Year) - Built Moonlake’s core **post-training and RL infrastructure** - Created scalable
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