VP of Robotic Foundation Model

Posted: 2026-04-16

As the VP of Robotic Foundation Model, you will lead the strategy, organization, and technical execution of our robotics foundation model team. This role is responsible for building and managing the team that turns that advancing the multimodal AI technology into robust, production-grade capability for real-world robots. You will define the roadmap for our robotic foundation model efforts, guide the end-to-end pipeline from teleoperation data to on-robot deployment, and ensure the team delivers models that are reliable, scalable, and useful in real operational environments, not just in research settings or proof-of-concept demos. You will work across AI, robotics, teleoperation, controls, hardware, and product teams to translate ambitious business and product goals into a practical technical strategy and high-performing engineering organization.

Leadership & Management

Build, lead, and grow the robotics foundation model team, including hiring, mentoring, performance management, and team capability development.

Define the team roadmap, set priorities, allocate resources, and drive execution toward business-critical milestones.

Partner with Robotics, Teleoperation, Hardware, Controls, Infrastructure, and Product teams to align technical work with product and operational goals.

Establish strong execution standards across planning, experiment review, quality, reproducibility, and deployment readiness.

Foundation Model Strategy & Architecture

Own the technical direction for robotic foundation models that integrate vision, language, robot state, and action.

Guide architecture decisions, training strategy, and system design to balance model capability, reliability, and deployment practicality.

Ensure model outputs integrate cleanly and safely with real robot control and autonomy systems.

Data & Learning Systems

Define data strategy for teleoperation and robot-operation data, including collection, curation, annotation, and dataset quality.

Oversee pipelines that transform raw multimodal robot data into training-ready datasets and useful evaluation assets.

Drive continuous learning approaches so models improve reliably as new deployment data is collected.