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.

Requirements

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 systems engineering teams.
Experience in robotics, perception systems, or autonomous driving is preferred.

Benefits

GM offers a variety of health and wellbeing benefit programs.
Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learning infrastructuremodel integrationCI/CD pipelinesautomated testingmodel validationperformance regression checksexperiment trackingdata processingmodel packagingproduction rollout

Soft Skills

strong communicationcollaborationmentorshipcode reviewsdesign reviews