Staff ML Engineer - Embodied AI Onboard Autonomy

generalmotors; Sunnyvale (CA); Hybrid; USD 180,000 - 280,000; Full time

Benefits offered by this job

Hybrid work arrangement

Relocation benefits

Company vehicle program

Job summary

General Motors is seeking a Staff AI/ML Engineer for the Onboard Embodied AI team to design, architect, and deploy end-to-end ML solutions that translate sensor data into safe driving decisions. The role emphasizes real-time onboard inference and collaboration with cross-functional teams in a hybrid setting.

You will mentor engineers, influence ML roadmaps, and help shape onboard AI capabilities for autonomous vehicles at GM's Mountain View Bay Area center.

Qualifications

• Master's or Ph.D. in ML, Robotics, CS, EE, or related field.
• 4+ years with large-scale Foundation Models (LLMs, VLAs, vision-focused models).
• Experience deploying advanced ML systems in real-time onboard apps.

Responsibilities

• Design, develop, and deploy onboard ML models for real-time inference and driving performance.
• Lead complex ML projects from conception to onboard deployment with emphasis on scalability and safety.
• Advise on neural architectures, training methodologies, and inference optimization for onboard use.

Skills

Foundation Models

LLMs

Vision models

Real-time inference

Python

C++

PyTorch

TensorFlow

JAX

Education

Master's or Ph.D. in ML/Robotics/CS/EE

Tools

PyTorch

TensorFlow

JAX

Python

C++

Job description

Job Description

Are you passionate about accelerating the future of autonomous driving? Join the Embodied AI team at General Motors. Our team is developing and deploying machine learning solutions that support safe and reliable autonomous vehicle behavior across real-world scenarios.

As a Staff AI/ML Engineer 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.

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

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.

• $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.

About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

Why Join Us

We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.

Benefits Overview

From day one, we're looking out for your well-being-at work and at home-so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.

Non-Discrimination and Equal Employment Opportunities (U.S.)

General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.

All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.

We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.

Accommodations

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