Senior Machine Learning Perception Engineer - Fallback Driving System
General Motors
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Job 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. Weāre turning todayās impossible into tomorrowās standard āfrom breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features.Ā Ā
Every day, our products move millions of peopleĀ as we aim to makeĀ driving safer, smarter, and more connected, shaping the future of transportation on a global scale.
As a Senior Machine Learning EngineerĀ onĀ the State Estimation and Mapping (SEAM) organization, you will develop and improve the MLĀ perceptionĀ model that powers the secondary (fallback) autonomy stack for Super Cruise 3. You will focus on building robustĀ perceptionĀ from multiāmodalĀ camera, lidar, and radar data so the vehicle can safely bring itself to a stop when the primary autonomy stack is unavailable.Ā
You will lead the design, implementation, and continuous improvement of ML models for object detection, segmentation, tracking, and prediction, working closely with partner teams acrossĀ perception, planning, controls, and safety.Ā
What You'll Do
Design, train, and evaluate MLĀ perceptionĀ models for object detection, semantic/instance segmentation, tracking, and shortāhorizon prediction using multiāmodal camera, lidar, and radar data.Ā
Develop andĀ maintainĀ the secondary stackĀ perceptionĀ model that enables the fallback autonomy system to safely bring the vehicle to a minimal risk condition when the primary system experiences a fault.Ā
Define clear ML success metrics (e.g., precision/recall, latency, robustness under edge cases) and drive systematic experimentation to improve model performance against those metrics.Ā
Analyze largeāscale datasets, curate challenging scenarios, and build dataĀ selectionĀ and labeling strategies that improve robustness for longātail and degradedāsensor conditions.Ā
Implement efficient training and inference pipelines, including model optimization techniques (e.g., pruning, quantization, distillation) to meet onāvehicle compute and latency budgets.Ā
Collaborate with software and infra engineers to integrate models into production systems, including interfaces, configuration, deployment, monitoring, and regression safeguards.Ā
Partner with Safety, Systems Engineering, and Product to translate system requirements into concrete ML model requirements, metrics, and validation criteria.Ā
Contribute to verification and validation strategies for the fallbackĀ perceptionĀ model, including offline evaluation, simulation, hardwareāinātheāloop, and onāroad testing.Ā
Participate in code reviews, promote ML and software engineering best practices, and provide technical mentorship to other engineers.Ā
Qualifications
BS, MS, or PhD in Machine Learning, Robotics, Computer Science, or a related technical field; or equivalent practical experience building MLĀ perceptionĀ systems.Ā
3ā5 years of experience developing ML solutions inĀ perception, prediction, and/or autonomous driving or related domains.Ā
Strong experience with multiāmodal sensor data (camera, lidar, radar), including data preprocessing, synchronization, and fusion.Ā
DeepĀ expertiseĀ in modern deep learning forĀ perception, such as convolutional and transformerābased architectures for:Ā
2D/3D object detectionĀ
Semantic and instance segmentationĀ
Multiāobject tracking and motion predictionĀ
ProficiencyĀ in at least one major ML framework (e.g.,Ā PyTorch, TensorFlow, JAX) and Python for model development, training, and analysis.Ā
Solid software engineering skills, including experience working in C++ or similar languages in large, collaborative codebases.Ā
Demonstrated ability to define ML metrics, design experiments, and systematically improve model performance and robustness.Ā
Strong problemāsolving, communication, and crossāfunctional collaboration skills.Ā
Selfāmotivated, with a passion for autonomous driving technology and its potential impact on safety and mobility.Ā
Nice to have
Experience deploying ML models on embedded or resourceāconstrained platforms, including model optimization and performance tuning for realātime inference.Ā
Experience with AV/ADASĀ perceptionĀ stacks, robotics, or ROS.Ā
Familiarity with safetyācritical systems and development practices.Ā
Experience with largeāscale data pipelines, labeling workflows, and experiment management for ML.Ā
Remote:Ā This role is based remotely but if you live within a 50-mile radius of Atlanta, Austin, Detroit, Warren,Ā MilfordĀ or Mountain View, you are expected to report to that location three timesĀ perĀ week, at minimum.Ā
CompensationĀ : The compensation information is a good faith estimate only. It is based on what a successful applicant might be paidĀ in accordance withĀ applicable state laws. The compensation may not beĀ representative for positionsĀ locatedĀ outside of the California Bay Area.Ā
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.Ā
Bonus Potential: An incentiveĀ payĀ program offers payouts based on company performance, job level, and individual performance.
Benefits:Ā Ā
Benefits: GM offers a variety of health and wellbeing benefit programs.Ā Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuitionĀ assistanceĀ programs, employeeĀ assistanceĀ program, GM vehicle discounts and more.Ā
This job may be eligible forĀ relocationĀ benefits.Ā
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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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