Senior AI / ML Engineer - Embodied AI

Description
At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We're turning today's impossible into tomorrow's standard -from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features.
Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale.
Role
As a Senior AI/ML Engineer in the Embodied AI organization, you will develop and deploy machine learning solutions contributing to autonomous driving performance. You will contribute to designing and improving ML models that support safe and reliable vehicle behavior across real-world scenarios.
You will collaborate closely with senior engineers and cross-functional teams to translate research and technical concepts into production-ready solutions while contributing to engineering best practices and delivery execution.
What You'll Do

• Develop and improve ML solutions aligned with GM's autonomous driving objectives.
• Apply techniques such as unsupervised pre-training, imitation learning, reinforcement learning, model scaling/selection, foundation modeling, to solve problems in object detection/tracking/classification, trajectory generation, and safe AI.
• Collaborate with cross-functional teams to deploy models into onboard driving systems.
• Implement and evaluate models, incorporating research advancements into practical applications.
• Contribute to taking ML solutions from experimentation through deployment and production support.
• Participate in code reviews, documentation, and technical discussions to support engineering quality and knowledge sharing.

Your Skills & Abilities

• Bachelor's or Master's degree in Computer Science, Robotics, Machine Learning, or related field.
• Experience applying machine learning techniques to real-world systems or large-scale datasets.
• Proficiency in PyTorch and Python.
• Experience working with model training pipelines or large-scale data processing workflows.
• Strong data processing skills using tools such as NumPy, Pandas, and Apache Spark.
• Ability to collaborate effectively within cross-functional engineering teams.
• Experience deploying ML models into production or working within production ML environments preferred.

Remote: This role is based remotely but if you live within a 50-mile radius of Austin, Detroit, Warren, Milford or Mountain View, you are expected to report to that location three times per week, at minimum.
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 the California Bay Area.

• The salary range for this role is $170,600.00 to $261,300.00. 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.

Benefits:

• Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

This job may be eligible for relocation benefits.
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About GM
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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.
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