Staff ML Engineer - Embodied AI Onboard Autonomy

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.

Key Responsibilities:

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.

Your Skills & Abilities:

Master's or Ph.D. in Machine Learning, Robotics, Computer Science, Electrical Engineering, or a related technical field.

4+ years of experience working with large-scale Foundation Models, including LLMs, VLAs and vision-focused models.

Extensive experience developing and deploying advanced ML systems, particularly in end-to-end real-time onboard applications.

Proven track record as a technical expert in developing robust deep learning models that directly map sensor data to actionable outputs within safety-critical systems.

Deep expertise in 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.

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 $180,000 to $280,000. 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.

Relocation: This job may be eligible for relocation benefits.

Company Vehicle: Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate. Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.

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