Member of Technical Staff — Agent Post-Training
This role involves leading reinforcement learning infrastructure and model post-training for multimodal, vision-language, and code-generating agents at Moonlake, focusing on building pipelines, distributed training systems, and improving agent performance in 3D environments.
- Compensation
- Not disclosed
- Location
- San Francisco, CA
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- 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
- 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
Nice to have
- Experience at a frontier AI lab or organization operating large-scale training systems
- Experience with code-model post-training or autonomous coding agents
- Experience with multimodal models, robotics, simulation, or embodied AI
- Experience designing verifiable rewards or outcome-based training systems
- Experience scaling RL workloads across large GPU clusters
Full description
Introducing Moonlake, AI for creating world simulations.
About Moonlake
Moonlake is building the frontier of interactive world models: systems that generate, simulate, and reason over 3D environments for robotics, embodied AI, and interactive applications.
We develop the infrastructure that enables intelligent systems to learn, evaluate, and interact within realistic virtual environments before operating in the physical world.
Our work sits at the intersection of:
- Robotics
- Embodied AI
- Interactive 3D Worlds
- World Models
- Simulation Infrastructure
- Physical AI
Moonlake is building the next generation of AI infrastructure for interactive digital worlds. Our mission is to enable anyone to create, simulate, and interact with rich environments using natural language and multimodal inputs, turning simple ideas into worlds with structure, physics, and intelligent behavior.
Our team has raised $50M in seed funding from NVIDIA Ventures, Threshold Ventures, AIX Ventures, and notable angels including Naval Ravikant and Jeff Dean to build the foundational layer for the future of AI—powering everything from robotics training and simulation to digital twins and interactive environments.
We are looking for exceptional engineers to help build the simulation systems that will power the next generation of robotics and embodied intelligence.
##
The Role
We are hiring a Member of Technical Staff to lead reinforcement learning infrastructure and model post-training.
You will work closely with Qi and the research team to improve large vision-language and code-generating agents through fine-tuning, reinforcement learning, trajectory data, and scalable evaluation.
Moonlake already has deep expertise in 3D and world-building. This role adds the model-training experience needed to systematically improve agent performance and prepare the company for larger-scale RL across both digital and physical environments.
We are looking for a full-stack researcher and engineer who understands the complete training system and can make strong judgments about when training is necessary, which methods are likely to work, and what not to pursue.
##
What You’ll Do
- 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