The Role
We are hiring a Member of Technical Staff to lead reinforcement learning infrastructure and agentic model post-training.
You will 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.
Moonlake already has deep expertise in 3D and world-building. This role adds the model-training experience needed to systematically improve agent performance and prepare the company for larger-scale RL across both digital and physical environments.
We are 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, and 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
What Success Looks LikeWithin your first year, you will have:
- Built Moonlake's core post-training and RL infrastructure
- Created scalable systems for learning from agent trajectories
- Delivered measurable improvements in agent quality and task completion
- Helped the team determine which problems require training and which do not
- Established a reliable foundation for larger-scale agent and embodied-model training
Why This Role MattersMoonlake's agents must do more than generate content. They must understand complex requests, reason across vision and language, write and execute code, operate tools, build interactive worlds, and recover from mistakes.
This role will build the training systems that allow those agents to continuously improve.
We are committed to being an on-site, in-person team currently based in San Francisco.
