LLM Researcher (+ Equity) at well-funded seed-stage robotics AI startup
About the role
The role is a hands-on research position focusing on developing foundation models for robotics, applying advanced techniques to video diffusion models to enable general-purpose autonomous policies.
Responsibilities
- Execute end-to-end post-training workflows including SFT, RLHF, and DPO to align robot policy behaviors.
- Develop and optimize scalable training pipelines for large-scale video diffusion and foundation models.
- Rapidly iterate on model architectures and training infrastructure to improve zero-shot generalization in new environments.
Requirements
Extensive hands-on experience in post-training LLMs, specifically focused on alignment, reasoning, or multi-modal learning.
Prolific coding ability with a track record of shipping research code, open-source contributions, or production ML systems.
Qualifications
Hungry, early-career researcher excited by the prospect of working in-person in San Francisco within a fast-moving, high-stakes startup.
Skills
Hands-on experience with post-training LLMs, particularly in areas such as alignment, reasoning, or multi-modal learning.
Benefits
Work at the cutting edge of physical AI by applying LLM-style scaling and generalization to humanoid robot autonomy.
Pay
Salary Not Disclosed + Equity
Schedule
Full-time
Company Description
Well-funded seed-stage robotics AI startup