Senior AI/ML Engineer - Future Sensing, Embodied AI

General Motors•Washington, DC

2d•$182,400 - $250,600•Remote

About The Position

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. 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 Senior AI/ML Future Sensing Engineer in the Embodied AI organization, you will develop and evaluate machine learning solutions contributing to future sensing architecture decisions and autonomous driving performance. You will contribute to designing and improving ML and perception models that support safe and reliable vehicle behavior across real-world scenarios, while helping connect sensing choices to measurable performance outcomes. You will collaborate closely with senior engineers and cross-functional teams to translate research and technical concepts into production-ready or production-informing solutions while contributing to engineering best practices, technical analyses, and delivery execution.

Requirements

Bachelor’s or Master’s degree in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a related field
Experience applying machine learning techniques to real-world systems or large-scale datasets
Experience building AI/ML or perception systems in autonomy, robotics, computer vision, or related domains
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
Experience with model validation, debugging, and failure analysis in ML or perception settings
Experience with one or more perception domains such as object detection, segmentation, tracking, reconstruction, localization, or sensor fusion
Ability to collaborate effectively within cross-functional engineering teams.

Nice To Haves

Experience with perception sensors including cameras, radar, and lidar
Experience with multi-modal sensor fusion and system integration
Experience with production ML pipelines, model optimization, and performance tuning
Experience with simulation, synthetic data, or scenario-based evaluation
Experience with architecting sensory systems or contributing to sensor placement and configuration studies
Experience deploying ML models into production or working within production ML environments
Experience in automotive, robotics, or safety-critical ML applications.

Responsibilities

Develop and improve AI/ML solutions aligned with GM’s autonomous driving and future sensing objectives
Apply techniques such as unsupervised pre-training, imitation learning, reinforcement learning, model scaling and selection, and foundation modeling to solve problems in object detection, tracking, classification, perception, and safe AI
Develop and evaluate perception models and components for sensing studies involving cameras, lidar, radar, and multi-modal sensor fusion
Implement and evaluate models, incorporating research advancements into practical applications
Contribute to model training, fine-tuning, validation, debugging, and performance optimization for perception and sensor-fusion tasks
Help define and implement robust metrics for detection, reconstruction, localization support, semantic labeling, and model robustness under varied environmental conditions
Work with real and synthetic data to evaluate sensing tradeoffs across weather, lighting, occlusion, sensor noise, clutter, and near-field versus long-range scenarios
Contribute to production pipelines and technical workflows spanning data loading, model evaluation, error analysis, and deployment-oriented support
Collaborate with cross-functional teams to integrate models and algorithms into onboard driving systems and future sensing evaluation workflows
Participate in code reviews, documentation, and technical discussions to support engineering quality and knowledge sharing.