Teams at General Motors

Build and own scalable hybrid human/machine data-labeling platform for autonomous vehicles. Define roadmap, design APIs/UIs, integrate ML-driven annotation, improve data quality, and ship full-stack features across frontend, backend, and ML services.

Develop data-centric machine learning systems for autonomous driving, including dataset curation, sampling, mining, evaluation, foundation-model training, and scaling-law studies. Train models across large multi-GPU and multi-node datasets using real and synthetic driving data. Investigate failures, improve data quality, define predictive offline metrics, and collaborate on deploying models into onboard driving systems. The role applies self-supervised learning, imitation learning, reinforcement learning, and foundation-model fine-tuning to perception, trajectory generation, and driving behavior.

Leads software release program management for complex vehicle software. Builds and develops a team of technical program managers, establishes release standards, improves release frequency and predictability, manages risks and dependencies, coordinates validation and OTA delivery, and strengthens readiness, quality, governance, and post-release improvement processes. Partners with engineering, product, quality, security, configuration management, and senior leadership.