Member of Technical Staff — RL & Agentic Model Training

San Francisco Bay Areafull time

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senior

H1B: no

Embedding VC Member of Technical Staff at Embedding VC leads reinforcement learning infrastructure and agentic model post-training to improve large vision-language and code-generating agents. The role involves building RL pipelines, developing training infrastructure, and scaling distributed training across multi-GPU environments.

Responsibilities

We are hiring a Member of Technical Staff to lead reinforcement learning infrastructure and agentic model post-training.
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.
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

Qualification

Familiarity with supervised fine-tuningExperience with code-generation agentsAbility to work across dataExperience with multimodal modelsWhat Success Looks LikeWithin your first year

Required

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

Preferred

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
What Success Looks Like
Within your first year, you will have:
Built Moonlake’s core post-training and RL infrastructure