Senior Member of Technical Staff: Multimodal Post-Training

About the role

You'll be a senior technical leader shaping how our foundation models become capable, aligned robot policies. Working alongside our AI leadership and a world-class team, you'll drive key work across supervised fine-tuning (SFT), preference optimization (RLHF/DPO-style), distillation, and reinforcement learning (RL). Success will be measured by policies that act more reliably on real robots in the real world.

Responsibilities

• Post-Training Strategy: Help shape the post-training recipe—SFT, preference tuning (RLHF/DPO-style), distillation, and RL—and own key stages of how each turns a strong base model into a deployable policy.
• Reasoning, Tool Use & Multimodality: Push grounded reasoning, tool use, and multimodal understanding for models that must act in the physical world, not only answer.
• Behavior & Alignment: Align model behavior with product and safety requirements across diverse tasks and embodiments.
• Evaluation Methodology: Strengthen the eval and experimentation methodology that separates real gains from noise—spanning offline metrics and on-robot performance.
• Technical Leadership: Set the technical bar for post-training, mentor the team, and partner with data teams on what data most improves behavior.

Requirements

• Post-Training Depth: Deep hands-on experience post-training large multimodal models—SFT, RLHF/DPO-style preference methods, distillation—with results you have shipped or published.
• Reinforcement Learning: Strong command of RL for large models (reward modeling, policy optimization) and its practical failure modes.
• Frontier-Scale Models: Experience working with multimodal LLMs or vision-language-action (VLA) models at frontier scale.
• Program Ownership: A record of owning end-to-end model-improvement programs and setting technical direction others follow.
• Measurement & Grounding: Excellent judgment about what to measure, and the instinct to debug across the stack.

Preferred Qualifications

• Experience with embodied AI, robotics, or vision-language-action models (or strong desire to move from LLMs into the physical world).
• Familiarity with evaluating open-ended, real-world task performance.
• Contributions to influential models, papers, or open-source post-training stacks.

Benefits

• Competitive total compensation program, including salary, annual cash bonus, and company equity.
• Company-subsidized insurance programs.
• 401(k) with company match.
• Flexible PTO.
• Daily lunch.