• Define and champion the overall AI strategy and roadmap in alignment with the company's product vision and business goals. • Lead the selection and integration of advanced machine learning models, algorithms, and deep learning techniques relevant to embodied AI (e.g., perception, manipulation, natural language understanding). • Foster a culture of innovation, rigorous research, and responsible AI development that supports the team's ongoing success. • Ensure that all AI systems adhere to the highest standards of safety, privacy, and ethical guidelines. • Partner with Hardware and Robot Software leadership to ensure the sensor suite and embedded compute are optimized for current and future AI model architectures. • Oversee the design, development, and deployment of on-robot and cloud-based AI systems, leveraging the existing team's expertise. • Establish MLOps best practices for efficient model training, deployment, monitoring, and iteration. • Drive research into novel AI solutions for complex robotics challenges, such as real-time planning, human-robot interaction, and adaptive motor control. • Deliver production-ready AI models that meet performance, latency, and reliability requirements for commercial deployment. • Spearhead the Sim2Real strategy, utilizing physics-based simulation (e.g., MuJoCo, Isaac Sim) to train robust policies that transfer seamlessly to hardware. • Accelerate our data flywheel strategy to combine teleoperation, synthetic data, and fleet learning to expand the fleet performance and reliability. • Champion the existing team's talent and represent their needs to senior leadership. • Develop a comprehensive hiring plan to staff and scale the AI team, including defining required roles (e.g., ML Engineers, Data Scientists, AI Researchers). • Lead recruitment, mentorship, and career development for all members of the AI team. • Manage the budget allocated for team salaries, equipment, and necessary infrastructure. • Forecast and manage the budget for cloud computing resources (GPUs, TPUs) necessary for large-scale model training and experimentation. • Secure and allocate funding for specialized datasets and data annotation services. • Evaluate and procure necessary software licenses and tools for AI development and simulation. • Regularly report on expenditure and resource utilization to senior leadership.