Staff Engineer - RL & Multimodal World Models

HellaGoodAudio; San Francisco (CA); In loco; USD 180.000 - 280.000; Tempo pieno

Descrizione del lavoro

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

Moonlake has deep expertise in 3D and world-building. This role adds model-training experience to systematically improve agent performance and prepare for larger-scale RL across digital and

Competenze

• Experience training large language, vision-language, multimodal, or code models.
• Strong experience in reinforcement learning and 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 and tool-using systems.
• Strong Python skills and experience with PyTorch, JAX, or similar frameworks.
• Ability to work across data, models, environments, rewards, evaluation, and infrastructure.
• Strong research judgment and a bias toward building reliable 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

Reinforcement learning

Post-training

Distributed training

PyTorch/JAX

Python

Code generation

Agent tooling

Research judgment

Strumenti

Linux

Descrizione del lavoro

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

Moonlake has deep expertise in 3D and world-building. This role adds model-training experience to systematically improve agent performance and prepare for larger-scale RL across digital and