Top AI & Machine Learning Jobs

The Principal AI/ML Engineer will lead the AV ML Infra team, overseeing scalable ML infrastructure, project ownership, collaboration, and team mentorship in AI and ML innovation at GM.

As an ML Infrastructure Engineer, design backend components, enhance platform performance, and drive system improvements while collaborating across teams.

As a Senior AI/ML Scientist, you'll integrate AI/ML into engineering tools, develop predictive models, analyze data, collaborate with teams, and establish best practices in a hybrid role.

Lead applied machine learning research to improve validation of ML components in autonomous vehicles, focusing on simulation-led strategies and automating validation processes.

As a Senior ML Engineer, you'll design and deploy advanced machine learning systems for autonomous vehicles, ensuring real-time performance and robustness in diverse conditions. You'll lead technical projects, mentor engineers, and collaborate across teams to enhance onboard capabilities.

As a Senior Machine Learning Engineer, you will manage the entire lifecycle of safety-critical ML perception models, optimize performance, and ensure reliable deployment on embedded systems.

As a Staff AI/ML Engineer, you'll enhance developer productivity, mentor engineers, drive best practices, and implement scalable solutions for AV tools and services.

Lead design and development of scalable, high-performance ML training infrastructure. Drive distributed training performance optimization, observability, and developer experience. Own cross-functional infrastructure initiatives, set technical direction and standards, and mentor engineers to deliver platform capabilities that support large-scale model training.

The role involves developing quantization and compression strategies for AV models, conducting numerical sensitivity analyses, and creating automated tooling for low-precision deployments, emphasizing safety and performance.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.