Moonlake AI
Member of Technical Staff — Post-Training Infrastructure
San Francisco, CA · Staff+
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About the role
- 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.
- 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.
Responsibilities
- Design rewards, verifiers, and evaluations for long-horizon agent tasks
- Experience building distributed training or high-throughput inference systems
Requirements
- 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
Nice to have
- 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
- Built Moonlake's core post-training and RL infrastructure
- Delivered measurable improvements in agent quality and task completion
- Helped the team determine which problems require training and which do not
Benefits
- 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
- Real-world experience training large language, vision-language, multimodal, or code models
- Strong experience in reinforcement learning, post-training, or large-scale fine-tuning
Company info
- We are committed to being an on-site, in-person team currently based in San Francisco.
- We are hiring a Member of Technical Staff to lead reinforcement learning infrastructure and model post-training.
This listing is sourced directly from Moonlake AI's careers page and normalized into a canonical job model.