VP of Robotic Foundation Model

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. Key Responsibilities 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. Deployment, Evaluation & Safety Lead deployment of trained models onto embedded and edge platforms such as Jetson-class systems. Define evaluation frameworks, KPIs, and review mechanisms for model quality, autonomy performance, safety, and operational robustness. Ensure failures observed in testing or the field are systematically analyzed and translated into model, data, or system improvements. Collaboration & External Representation Act as the company’s technical leader for robotics foundation model development, influencing adjacent teams and executive decision-making. Represent the team in discussions with research partners, technology vendors, and external collaborators. Stay current with advances in multimodal AI, robotics learning, and large-scale model systems, and apply relevant insights to the team roadmap. Qualifications Professional Experience Proven experience leading or managing high-performing ML, robotics AI, or multimodal foundation model teams. Strong track record of taking advanced AI or robotics systems from research or prototype stage into reliable real-world operation. Experience owning team execution, technical direction, prioritization, and stakeholder alignment for complex engineering programs. Demonstrated ability to lead in environments where both deep technical contribution and strong management are required. Technical Skills Multimodal / Foundation Model Expertise Deep expertise in designing, training, and evaluating large-scale multimodal models, such as vision-language, vision-language-action, or related transformer-based systems. Strong understanding of modern training paradigms, model scaling, fine-tuning, representation learning, and inference optimization. Robotics & Real-World Deployment Experience integrating AI/ML systems with physical robots under real-world operational constraints. Strong understanding of robotics software stacks, robot sensing, action representation, and the practical realities of deploying learned systems on hardware. Familiarity with robotics middleware such as ROS1/2 and with embedded or edge AI deployment platforms such as Jetson. Data & Infrastructure Strong experience building data pipelines and training systems for large, complex multimodal datasets including images, video, text, robot trajectories, and sensor logs. Familiarity with distributed training frameworks and production ML infrastructure. Systems Thinking Solid understanding of how high-level model decisions interact with low-level robot execution, control, safety, and system boundaries. Strong engineering judgment in balancing research ambition with deployment practicality. Soft Skills & Culture Fit Ownership mentality : takes responsibility for outcomes, not only technical ideas. Managerial maturity : able to lead, coach, evaluate, and grow a strong team. User-centric mindset : understands how model capabilities must translate into useful, reliable product behavior for customers and operators. Comfortable in a high-performance, high-accountability environment. Strong communication skills in English; Japanese proficiency is a plus. Supplementary Materials This section is optional. To support a thorough evaluation of your candidacy, we encourage you to provide clear and detailed evidence of both: your leadership and management experience in building or guiding strong technical teams, and your direct technical contribution to advanced AI-driven robotics or multimodal model systems. Please ensure your response addresses the following points: Project Portfolio / Demo Links Links to notable projects, repositories, publications, or videos that demonstrate real robotics AI or foundation-model-related work. Technical Contribution Details Clear explanation of your role in model design, dataset strategy, training systems, deployment, and integration with robot platforms. Leadership Scope Description of team size, management responsibilities, hiring or mentoring scope, and how you drove execution across functions. Operational Results Concrete examples showing how your work led to robust real-world performance, improved autonomy, or successful deployment beyond PoC or research-only environments.