Member of Technical Staff — Agentic Model Training

The role

You will lead reinforcement learning infrastructure, model post-training, and the development of intelligent agents. You will work on improving large vision-language, multimodal, and code-generating agents through fine-tuning, reinforcement learning, and reward design.

What you will do

• 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

Required qualifications

• 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

About the company

Moonlake is building the frontier of interactive world models that generate, simulate, and reason over 3D environments. The company is building the next generation of AI infrastructure for interactive digital worlds.