Member of Technical Staff — Agentic Model Training

We are hiring a Member of Technical Staff focused on Agentic Model Training to lead reinforcement learning infrastructure, model post-training, and the development of intelligent agents capable of operating across complex digital and physical environments.

You will work closely with Qi and the research team to improve large vision-language, multimodal, and code-generating agents through fine-tuning, reinforcement learning, trajectory data, reward design, 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 reasoning, planning, tool use, code generation, and long-horizon task execution while preparing the company for larger-scale reinforcement learning across interactive worlds, simulation, and embodied AI.

We are looking for a full-stack researcher and engineer who understands the complete model-training lifecycle, from data collection and training infrastructure to reward design, evaluation, and deployment. You should have strong research judgment, understand when training is necessary, which methods are likely to work, and what not to pursue.