Staff AI/ML Engineer - Future Sensing, Embodied AI

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 required 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. 

 What You’ll Do  

• 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.