Staff AI/ML Engineer - Future Sensing, Embodied AI

GM•Sunnyvale, CA

21h•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 Staff AI/ML Future Sensing Engineer in the Embodied AI organization, you will serve as a senior individual contributor driving end-to-end technical work that informs next-generation sensing architecture decisions. You will help define and evaluate machine learning and perception solutions that directly impact autonomous driving performance, with emphasis on future sensing architectures, multi-modal sensor fusion, system integration, and the technical evidence to support sensor and compute decisions. In this role, you will partner closely with cross-functional engineering teams, contribute to core technical direction within your domain, and support the growth of engineers through technical collaboration and mentorship. You will help translate research into scalable onboard ML and perception solutions while contributing to the continuous improvement of GM’s autonomy stack and sensing strategy.

Requirements

Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a related field
Strong experience building and scaling AI/ML systems for perception, autonomy, robotics, or related real-world systems
Deep hands-on experience with modern deep learning frameworks such as PyTorch and strong proficiency in Python
Experience working with model training pipelines, large-scale data workflows, and infrastructure enabling efficient model iteration across teams
Strong data processing skills using tools such as NumPy, Pandas, and Apache Spark
Strong experience with model validation, debugging, performance optimization, and error analysis under real-world constraints and timelines
Strong experience with multi-modal sensor fusion and perception pipelines using cameras, radar, lidar, or related sensing modalities
Experience defining metrics and evaluation methodologies for perception or autonomy systems
Strong communication skills enabling effective collaboration across engineering teams
Experience deploying or preparing ML models for production environments and understanding end-to-end deployment workflows.

Nice To Haves

Experience in robotics or autonomous driving systems
Experience with architecting perception or sensory systems for automotive, robotics, or safety-critical platforms
Experience with system integration across sensors, calibration, compute, and onboard software pipelines
Experience with simulation, synthetic data, and sim-to-road evaluation workflows
Technical leadership experience including mentoring engineers and shaping major workstreams from concept to execution.

Responsibilities

Design and implement AI/ML solutions aligned with GM’s autonomous driving and future sensing objectives
Lead end-to-end technical studies across sensor selection, sensor configuration, sensor placement, and multi-modal sensor fusion using cameras, lidar, radar, and related sensing modalities
Architect and evaluate perception models and pipelines for detection, reconstruction, tracking, localization support, semantic labeling, and uncertainty estimation
Drive definition of robust model-level and system-level metrics used to compare sensor configurations, quantify subsystem differences, and evaluate performance parity relative to existing architectures
Lead model development efforts spanning data curation, training, validation, performance optimization, debugging, and deployment-oriented analysis
Partner with simulation teams to define synthetic-data and sensor-model requirements needed to evaluate future sensing concepts under adverse weather, sensor noise, occlusions, clutter, and near-field versus long-range scenarios
Drive system integration thinking across sensing, calibration, compute, software architecture, and vehicle constraints
Translate ambiguous architecture questions into concrete experiments, technical recommendations, and clear go / no-go evidence packages
Design and build efficient infrastructure, pipelines, and tooling to support large-scale data processing, model training, evaluation, and rapid iteration across teams
Drive technical execution from prototyping through integration and readiness for production adoption, documenting learnings and best practices
Support and mentor engineers through technical collaboration and code reviews, fostering knowledge sharing and engineering excellence.