Senior Machine Learning Perception Engineer - Fallback Driving System

You will lead the design, implementation, and continuous improvement of ML models for object detection, segmentation, tracking, and prediction, working closely with partner teams acrossperception, planning, controls, and safety.

What You'll Do

Design, train, and evaluate MLperceptionmodels for object detection, semantic/instance segmentation, tracking, and short‑horizon prediction using multi‑modal camera, lidar, and radar data.

Develop andmaintainthe secondary stackperceptionmodel that enables the fallback autonomy system to safely bring the vehicle to a minimal risk condition when the primary system experiences a fault.

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.

Analyze large‑scale datasets, curate challenging scenarios, and build dataselectionand labeling strategies that improve robustness for long‑tail and degraded‑sensor conditions.

Implement efficient training and inference pipelines, including model optimization techniques (e.g., pruning, quantization, distillation) to meet on‑vehicle compute and latency budgets.

Collaborate with software and infra engineers to integrate models into production systems, including interfaces, configuration, deployment, monitoring, and regression safeguards.

Partner with Safety, Systems Engineering, and Product to translate system requirements into concrete ML model requirements, metrics, and validation criteria.

Contribute to verification and validation strategies for the fallbackperceptionmodel, including offline evaluation, simulation, hardware‑in‑the‑loop, and on‑road testing.

Participate in code reviews, promote ML and software engineering best practices, and provide technical mentorship to other engineers.

Qualifications

BS, MS, or PhD in Machine Learning, Robotics, Computer Science, or a related technical field; or equivalent practical experience building MLperceptionsystems.

3–5 years of experience developing ML solutions inperception, prediction, and/or autonomous driving or related domains.

Strong experience with multi‑modal sensor data (camera, lidar, radar), including data preprocessing, synchronization, and fusion.

Deepexpertisein modern deep learning forperception, such as convolutional and transformer‑based architectures for:

2D/3D object detection

Semantic and instance segmentation

Multi‑object tracking and motion prediction

Proficiencyin at least one major ML framework (e.g.,PyTorch, TensorFlow, JAX) and Python for model development, training, and analysis.

Solid software engineering skills, including experience working in C++ or similar languages in large, collaborative codebases.

Demonstrated ability to define ML metrics, design experiments, and systematically improve model performance and robustness.

Strong problem‑solving, communication, and cross‑functional collaboration skills.

Self‑motivated, with a passion for autonomous driving technology and its potential impact on safety and mobility.

Nice to have

Experience deploying ML models on embedded or resource‑constrained platforms, including model optimization and performance tuning for real‑time inference.

Experience with AV/ADASperceptionstacks, robotics, or ROS.

Familiarity with safety‑critical systems and development practices.

Experience with large‑scale data pipelines, labeling workflows, and experiment management for ML.

Remote: This role is based remotely but if you live within a 50-mile radius of Atlanta, 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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