RL & Agentic Model Training Engineer

Embedding; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Moonlake is hiring a Member of Technical Staff to lead reinforcement learning infrastructure and agentic model post-training. You will collaborate with Qi and the research team to enhance large vision-language and code-generating agents through fine-tuning, RL, trajectories, and scalable evaluation.

The role requires building robust training systems for both digital and physical environments. We are seeking a full-stack researcher who can assess training needs, select effective methods, and

Qualifications

• Strong Python skills and experience with PyTorch or JAX.
• Real-world experience training large language, vision-language, multimodal, or code models.
• 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.

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.

Skills

Python

Reinforcement learning

Large-scale ML systems

Multimodal modelling

Problem solving

Tools

PyTorch

JAX

Distributed training systems

Job description

Moonlake is hiring a Member of Technical Staff to lead reinforcement learning infrastructure and agentic model post-training. You will collaborate with Qi and the research team to enhance large vision-language and code-generating agents through fine-tuning, RL, trajectories, and scalable evaluation.

The role requires building robust training systems for both digital and physical environments. We are seeking a full-stack researcher who can assess training needs, select effective methods, and