Staff ML Infrastructure Engineer - Embodied AI Offboard Perception

About the Role

The Embodied AI team is committed to innovation and excellence, and you will be expected to contribute to the team's technical direction and growth. You will also have the opportunity to mentor junior engineers and help shape the company's approach to machine learning infrastructure. With a strong focus on collaboration and technical leadership, this role is ideal for engineers who are passionate about autonomous driving and want to make a real impact on the industry.

General Motors is a leader in the automotive industry, and its commitment to innovation and customer satisfaction is unparalleled. As a member of the Embodied AI team, you will be part of a dynamic and fast-paced environment that values creativity, innovation, and teamwork.

What You Will Do

• Design, build, and maintain ML infrastructure that enables rapid development, training, evaluation, and deployment of offboard perception models
• Own the integration of models into production systems, including packaging, validation, deployment, and rollout strategies
• Implement CI/CD pipelines for ML systems, including automated testing, model validation, performance regression checks, and deployment automation
• Establish model evaluation and observability frameworks, including training metrics, inference performance metrics, data quality checks, and production monitoring dashboards
• Develop infrastructure for experiment tracking and benchmarking, enabling teams to compare model architectures, datasets, hyperparameters, and training procedures in a reliable and repeatable way
• Support efficient dataset curation and ingestion pipelines that help prioritize high-value data, accelerate iteration cycles, and improve model performance on hard-edge cases
• Partner with ML engineers, researchers, and software teams to ensure models can be reliably integrated into larger autonomy stacks and production services at scale
• Define and enforce best practices for ML systems engineering, including reproducibility, configuration management, artifact management, security, and operational readiness
• Support technical collaboration through code reviews, design reviews, and mentorship, helping raise the quality and maintainability of ML infrastructure across the organization
• Collaborate with data scientists and engineers to identify opportunities for process improvements and implement changes that increase efficiency and productivity

What We Are Looking For

• Strong software engineering fundamentals, including experience building reliable, maintainable, and scalable production systems
• Proficiency in Python, with experience using ML and scientific computing libraries such as PyTorch, NumPy, and related tooling
• Experience building and supporting ML training and deployment pipelines, including data processing, experiment execution, model packaging, and production rollout
• Experience deploying ML models into production environments, with understanding of end-to-end workflows such as validation, serving, monitoring, and lifecycle management
• Familiarity with distributed training and large-scale compute infrastructure, including GPUs, cluster scheduling, and performance optimization for training workloads
• Experience with containerization, orchestration, and automation tools such as Docker, Kubernetes, and related technologies
• Strong understanding of machine learning principles, including supervised and unsupervised learning, deep learning, and reinforcement learning
• Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams
• Strong problem-solving skills, with the ability to debug complex issues and optimize system performance

Nice to Have

• Experience with cloud-based ML platforms, including AWS SageMaker, Google Cloud AI Platform, or Azure Machine Learning
• Familiarity with agile development methodologies, including Scrum or Kanban
• Experience with DevOps practices, including continuous integration, continuous deployment, and continuous monitoring
• Knowledge of cybersecurity principles and practices, including data encryption, access controls, and network security

Benefits and Perks

• Competitive salary and benefits package
• Opportunities for professional growth and career development
• Collaborative and dynamic work environment
• Flexible work arrangements, including remote work options
• Access to cutting-edge technologies and tools
• Comprehensive health insurance package, including medical, dental, and vision coverage
• 401(k) retirement plan with company match
• Paid time off and holidays
• Employee discounts on General Motors vehicles and products

• Be prepared to discuss your experience with machine learning infrastructure, including your proficiency in Python and experience with ML frameworks such as PyTorch.
• Highlight your ability to work effectively in a team environment, including your experience with collaboration tools such as GitHub or Jira.
• Show a strong understanding of machine learning principles, including supervised and unsupervised learning, deep learning, and reinforcement learning.
• Be prepared to discuss your experience with cloud-based ML platforms, including AWS SageMaker, Google Cloud AI Platform, or Azure Machine Learning.
• Use your cover letter to highlight your passion for autonomous driving and your desire to work on a team that is shaping the future of transportation.
• Be prepared to provide specific examples of your experience with DevOps practices, including continuous integration, continuous deployment, and continuous monitoring.