Staff ML Infrastructure Engineer - Embodied AI Offboard Perception

Job Description

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 ML Infra Engineer on the Offboard Perception team within the Embodied AI organization, you will be a senior engineer responsible for developing and deploying offboard machine learning solutions that deliver ground-truth–quality world estimates for multiple partner teams, including onboard model teams, simulation, and evaluation. The models you build will influence every stage of autonomous vehicle development—from training and validation to testing and safety. You will work closely with cross-functional engineering teams, help shape technical direction in your domain, and support other engineers' growth through collaboration and mentorship. You will also help transition research into scalable onboard ML capabilities while continuously improving the autonomy stack.

What You'll 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, 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.

Your Skills & Abilities

• 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, workflow schedulers, and CI/CD systems. 

• Experience with model observability and operational metrics, including training metrics, inference performance, reliability monitoring, and data/model drift detection. 

• Strong communication and collaboration skills, with the ability to work effectively across ML, software, data, and system