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About the Role
Moonlake is seeking a Member of Technical Staff to lead reinforcement learning infrastructure and agentic model post-training. You will collaborate with the research team to enhance large vision-language and code-generating agents through fine-tuning, reinforcement learning, trajectory data, and scalable evaluation. This role is crucial for systematically improving agent performance and preparing the company for large-scale RL.
Responsibilities
- 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
Requirements
- 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
- 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
Skills
- Reinforcement Learning
- Post-training
- Large-scale fine-tuning
- Distributed training
- High-throughput inference systems
- Supervised fine-tuning
- Preference optimization
- Reward modeling
- Agentic RL
- Code-generation agents
- Long-horizon evaluation
- Tool-using systems
- Python
- PyTorch
- JAX
- Multimodal models
- Robotics
- Simulation
- Embodied AI
Location
- San Francisco
Work Type
- On-site
Experience Level
- Member of Technical Staff
About the Company
- 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.
- Moonlake is building the next generation of AI infrastructure for interactive digital worlds.
- Our mission is to enable anyone to create, simulate, and interact with rich environments using natural language and multimodal inputs, turning simple ideas into worlds with structure, physics, and intelligent behavior.
- Our team has raised $50M in seed funding from NVIDIA Ventures, Threshold Ventures, AIX Ventures, and notable angels including Naval Ravikant and Jeff Dean to build the foundational layer for the future of AI—powering everything from robotics training and simulation to digital twins and interactive environments.