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. Were turning todays impossible into tomorrows 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.
As a Principal Technical Lead in Trajectory Generation within the 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 to reliably and safely 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.
Design and implement technologies that align our models to GMs mission.
Apply techniques such as RL, guidance, and selection to raise the performance of our models and provide a layer safety and reliability.
Collaborate with cross-functional teams to integrate & align distillation recipes of Robotics foundation models for onboard driving models.
Conduct research and stay updated on the latest advancements in AI frameworks and libraries.
Lead projects from ideation to deployment, documenting learnings and best practices along the way.
Mentor junior team members and contribute to a culture of knowledge sharing and continuous improvement.
Bachelors, Masters or PhD degree in Computer Science with a focus in Robotics and or Machine Learning, or a related field.
Proven experience working with large-scale Foundation Models and alignment methods to translate research into real-world impact.
Proven experience delivering applied research in the wild and maintaining best practices while working on tight deadlines.
Proficiency in frameworks such as PyTorch and languages such as python.
Proven experience in building and scaling large model training pipelines that are performant and enable quick iteration by distributed teams.
Strong data processing skills using tools like Numpy, Pandas, and Apache Spark.
Excellent communication skills to effectively collaborate with diverse teams and stakeholders.
Experience deploying foundation models into production environments and understanding the end-to-end process.
Previous experience in Robotics or Autonomous Driving.
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 $ 269,400.00 to $412,600.00. 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: 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.
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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