Member of Technical Staff — Agent Post-Training

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. The team is hiring a Member of Technical Staff to lead reinforcement learning infrastructure and model post-training, working 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. The company is committed to being an on-site, in-person team currently based in San Francisco.

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

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