Staff Engineer, RL & Agentic World-Model Training

Embedding VC; San Francisco (CA); In loco; USD 190.000 - 270.000; Tempo pieno

Descrizione del lavoro

Moonlake is building the next generation of AI infrastructure for interactive digital worlds. We are seeking exceptional engineers to lead reinforcement learning infrastructure and agentic model post-training, collaborating with the research team to improve large vision-language and code-generating agents through fine-tuning, RL, trajectory data, and scalable evaluation.

This role requires real-world experience with large models, distributed training, and tool-using systems, and offers the

Competenze

• 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.

Mansioni

• 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.

Conoscenze

Python

PyTorch

JAX

Reinforcement learning

Distributed training

Code generation

Multimodal models

Tool-using systems

Descrizione del lavoro

Moonlake is building the next generation of AI infrastructure for interactive digital worlds. We are seeking exceptional engineers to lead reinforcement learning infrastructure and agentic model post-training, collaborating with the research team to improve large vision-language and code-generating agents through fine-tuning, RL, trajectory data, and scalable evaluation.

This role requires real-world experience with large models, distributed training, and tool-using systems, and offers the