Staff AI/ML Engineer - Onboard Embodied AI

Job Description Role:As a Technical Lead in Machine Learning within the Onboard Embodied AI organization, you will be a senior individual contributor driving cutting-edge end-to-end machine learning solutions directly impacting autonomous driving performance. Your role is pivotal in designing, architecting, and deploying advanced ML models that translate raw sensor data into actionable driving behaviors, enabling vehicles to robustly navigate diverse real-world scenarios and conditions. You'll lead critical technical initiatives, collaborate closely with cross-functional teams, mentor ML engineers, and significantly shape the future of onboard ML capabilities. About the Organization: The Onboard Embodied AI team is at the forefront of developing groundbreaking onboard ML systems powering fully autonomous vehicles. We leverage modern end-to-end machine learning approaches with sophisticated neural networks trained from large-scale driving data and using state - of - the - art alignment approaches . Our solutions enable vehicles to understand complex, dynamic driving environments, handle uncertainty gracefully, and adapt seamlessly to changing conditions. Join a collaborative and innovative team redefining autonomy through state-of-the-art machine learning, delivering solutions that move beyond current technological boundaries. What You'll Do: • Drive the design, development, and deployment of advanced onboard ML models, delivering end-to-end solutions capable of real-time inference and robust autonomous driving performance. • Lead and architect complex machine learning projects, from conception through validation to onboard implementation, emphasizing scalability, robustness, and safety-critical operation. • Champion innovation in neural network architectures, training methodologies, and inference optimization strategies suited for real-time onboard deployment. • Provide technical mentorship and thought leadership, elevating engineering practices, and fostering ML innovation across teams. • Collaborate closely with multidisciplinary engineering groups, ensuring seamless integration of ML capabilities into autonomous vehicle systems. • Influence technical roadmaps, shaping strategic ML priorities aligned with company objectives and product milestones. Additional Job Description Your Skills & Abilities: • Master's or Ph.D. in Machine Learning, Robotics, Computer Science, Electrical Engineering, or a related technical field. • 8-10+ years of extensive experience developing and deploying advanced ML systems, particularly in end-to-end real-time onboard applications. • Proven track record as a technical leader and expert in developing robust deep learning models that directly map sensor data to actionable outputs within safety-critical systems. • Deep expertise in modern machine learning techniques, including state- of- the-art computer vision techniques, neural architectures, representation learning, real-time inference, model optimization, and robustness under uncertainty. • Strong software engineering proficiency , particularly Python and C++, alongside extensive hands-on experience with modern ML frameworks ( PyTorch , TensorFlow, JAX). • Excellent communication, collaboration, and mentoring abilities, comfortable influencing technical strategy and guiding ML engineering excellence across the organization. • Bonus: AV/ADAS experience is a big plus Compensation:The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington. • The salary range for this role is $186,200 to $285,300. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position. • Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance. Hybrid:This role is categorized as hybrid. This means the successful candidate is expected to report to the Mountain View Technical Center in the Bay Area three times per week, at minimum. 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