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.

Responsibilities

Build reinforcement learning 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 scalable systems for collecting, filtering, replaying, and learning from agent trajectories and interaction data; Design rewards, verifiers, and evaluation frameworks for complex, long-horizon agent tasks; Improve agents' ability to reason, plan, write and execute code, use tools, recover from errors, and complete complex workflows; Develop training methods that improve agent performance across interactive 3D environments, simulation, and embodied tasks; Scale distributed training, high-throughput inference, and rollout generation across multi-GPU environments; Build reliable systems for evaluating model performance, tracking training improvements, and identifying failure modes; Improve training reliability, reproducibility, observability, and computational efficiency; Work across models, data, environments, rewards, and infrastructure to drive measurable improvements in agent capabilities; Help define Moonlake's long-term strategy for agentic model training, robotics, and embodied intelligence

Requirements

Real-world experience training large language, vision-language, multimodal, or code-generating models; Strong experience in reinforcement learning, model post-training, or large-scale fine-tuning; Experience building distributed training infrastructure or high-throughput inference and rollout systems; Familiarity with supervised fine-tuning, preference optimization, reward modeling, and agentic reinforcement learning; Experience with code-generation agents, tool-using systems, or long-horizon agent evaluation; Strong Python skills and experience with PyTorch, JAX, or similar machine learning frameworks; Understanding of model training across data collection, trajectory generation, reward design, optimization, and evaluation; Ability to work across research and engineering to translate new training methods into reliable, scalable systems; Strong research judgment, technical independence, and a bias toward building and shipping reliable systems

Nice to have

Experience at a frontier AI lab or organization operating large-scale model-training systems; Experience with code-model post-training, autonomous coding agents, or tool-using language models; Experience training multimodal models, vision-language agents, or models operating in interactive environments; Experience with robotics, simulation, world models, or embodied AI; Experience designing verifiable rewards, outcome-based training systems, or long-horizon agent evaluations; Experience scaling reinforcement learning workloads and distributed training across large GPU clusters; Familiarity with trajectory-based learning, synthetic data generation, and agent-environment interaction loops