General Motors
Job Description
Hybrid. This role is categorized as hybrid. The successful candidate is expected to report to the GM Global Technical Center - Cole Engineering Center Podium or Mountain View Technical Center, CA at least three times per week, at minimum or other frequency dictated by the business. This job is eligible for relocation assistance. About the Team
The ML Orchestration team at GM builds and maintains the foundational infrastructure that powers ML workflows across the company. The core responsibility is the development and evolution of Roboflow, GMs in-house semantic orchestration platform designed to streamline and scale complex ML pipelines from experimentation to production. A key pillar is AI Lineagethe capability to track, visualize, and understand the entire lifecycle of ML artifacts, including data origin, model training runs, hyperparameters, code versions, and evaluation metrics. AI Lineage provides transparency, auditability, and reproducibility across ML systems, essential for debugging, model governance, regulatory compliance, and improving long-term model quality. Together, Roboflow and AI Lineage help engineers move faster with confidence while meeting safety and performance standards for autonomous vehicle development. Position Overview
We are seeking an experienced
Staff Machine Learning Engineer
to drive key initiatives within the ML Orchestration team. You will scale our internal ML platform, build automation and self-service tools, and ensure the reliability and efficiency of large-scale ML pipelines across GM. A major focus area is the development and evolution of
AI Lineage , our system for capturing, querying, and visualizing the full lifecycle of machine learning artifacts. You will help design lineage tracking for data transformations, model training, evaluation runs, and pipeline dependencies to enable transparency, reproducibility, debugging, and regulatory compliance across our ML ecosystem. Please note : This is an ML infrastructure engineering role. It does not involve training or applying machine learning models to specific business problems. Your impact comes from building core infrastructure products that empower ML and data science practitioners at GM to experiment, deploy, and manage ML workflows at scale. What Youll Be Doing
Design & Implementation: Architect, implement, and test scalable, cloud-native distributed systems using modern cloud platforms such as Google Cloud Platform (GCP) or Microsoft Azure. Build robust infrastructure to support large-scale ML workflows and data processing at GM. Project Ownership: Lead technical projects end-to-endfrom early design through production deployment. Shape the product roadmap and drive key architectural decisions, balancing performance, reliability, and long-term maintainability. Cross-Team Collaboration: Participate in design reviews, team planning, and code reviews. Collaborate across multiple engineering teams to deliver cohesive platform solutions. Manage dependencies and trade-offs. Mentorship & Recruiting: Foster technical excellence and growth. Interview candidates, onboard new hires, and mentor engineers and interns to help them grow technically and professionally. Minimum Qualifications
8+ years of industry experience, focused on large-scale distributed systems or cloud infrastructure. 3+ years of experience leading and delivering complex technical initiatives across teams. Strong programming skills in Python, C++, Go, or similar languages, with production-grade systems experience. Hands-on experience with relational and NoSQL databases. Proven ability to design, build, and maintain highly scalable systems in production environments. Bachelors, Masters, or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Physics, or related fieldor equivalent practical experience. Attention to detail, strong problem-solving skills, and a track record of building high-quality systems. Passion for autonomous vehicles, infrastructure engineering, and advancing ML platforms. Adaptability and a startup mindsetcomfortable with ambiguity and cross-functional responsibilities. Preferred Qualifications
Experience with GCP, Azure, or AWS. Familiarity with open-source ML orchestration tools such as Kubeflow, Flyte, Airflow, or similar. Experience with Kubernetes and container orchestration at scale. Understanding of ML pipelines, data lineage, model lifecycle management, and reproducibility challenges. Strong proficiency in Python, C++, or Golang. Contributions to open-source projects or relevant technical publications. Compensation & Benefits
Compensation: The base salary range is $195,000 - $298,000 per year. Actual base compensation will vary based on factors relevant to the position. The information is a good faith estimate and may not apply outside certain locations. Bonus Potential: An incentive pay program based on company, job level, and individual performance. Benefits: Health, dental, vision, HSA/FSA, retirement savings, paid leave, and additional benefit programs. About GM
General Motors envisions a future with Zero Crashes, Zero Emissions, and Zero Congestion, and strives to lead change that makes the world better, safer, and more equitable for all. EEO & Accommodations
GM is an equal opportunity employer. All employment decisions are made without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, veteran status, or any other protected status in accordance with laws. If you require accommodations during your job search or application, please contact us via email or phone as listed in the posting. #J-18808-Ljbffr
Hybrid. This role is categorized as hybrid. The successful candidate is expected to report to the GM Global Technical Center - Cole Engineering Center Podium or Mountain View Technical Center, CA at least three times per week, at minimum or other frequency dictated by the business. This job is eligible for relocation assistance. About the Team
The ML Orchestration team at GM builds and maintains the foundational infrastructure that powers ML workflows across the company. The core responsibility is the development and evolution of Roboflow, GMs in-house semantic orchestration platform designed to streamline and scale complex ML pipelines from experimentation to production. A key pillar is AI Lineagethe capability to track, visualize, and understand the entire lifecycle of ML artifacts, including data origin, model training runs, hyperparameters, code versions, and evaluation metrics. AI Lineage provides transparency, auditability, and reproducibility across ML systems, essential for debugging, model governance, regulatory compliance, and improving long-term model quality. Together, Roboflow and AI Lineage help engineers move faster with confidence while meeting safety and performance standards for autonomous vehicle development. Position Overview
We are seeking an experienced
Staff Machine Learning Engineer
to drive key initiatives within the ML Orchestration team. You will scale our internal ML platform, build automation and self-service tools, and ensure the reliability and efficiency of large-scale ML pipelines across GM. A major focus area is the development and evolution of
AI Lineage , our system for capturing, querying, and visualizing the full lifecycle of machine learning artifacts. You will help design lineage tracking for data transformations, model training, evaluation runs, and pipeline dependencies to enable transparency, reproducibility, debugging, and regulatory compliance across our ML ecosystem. Please note : This is an ML infrastructure engineering role. It does not involve training or applying machine learning models to specific business problems. Your impact comes from building core infrastructure products that empower ML and data science practitioners at GM to experiment, deploy, and manage ML workflows at scale. What Youll Be Doing
Design & Implementation: Architect, implement, and test scalable, cloud-native distributed systems using modern cloud platforms such as Google Cloud Platform (GCP) or Microsoft Azure. Build robust infrastructure to support large-scale ML workflows and data processing at GM. Project Ownership: Lead technical projects end-to-endfrom early design through production deployment. Shape the product roadmap and drive key architectural decisions, balancing performance, reliability, and long-term maintainability. Cross-Team Collaboration: Participate in design reviews, team planning, and code reviews. Collaborate across multiple engineering teams to deliver cohesive platform solutions. Manage dependencies and trade-offs. Mentorship & Recruiting: Foster technical excellence and growth. Interview candidates, onboard new hires, and mentor engineers and interns to help them grow technically and professionally. Minimum Qualifications
8+ years of industry experience, focused on large-scale distributed systems or cloud infrastructure. 3+ years of experience leading and delivering complex technical initiatives across teams. Strong programming skills in Python, C++, Go, or similar languages, with production-grade systems experience. Hands-on experience with relational and NoSQL databases. Proven ability to design, build, and maintain highly scalable systems in production environments. Bachelors, Masters, or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Physics, or related fieldor equivalent practical experience. Attention to detail, strong problem-solving skills, and a track record of building high-quality systems. Passion for autonomous vehicles, infrastructure engineering, and advancing ML platforms. Adaptability and a startup mindsetcomfortable with ambiguity and cross-functional responsibilities. Preferred Qualifications
Experience with GCP, Azure, or AWS. Familiarity with open-source ML orchestration tools such as Kubeflow, Flyte, Airflow, or similar. Experience with Kubernetes and container orchestration at scale. Understanding of ML pipelines, data lineage, model lifecycle management, and reproducibility challenges. Strong proficiency in Python, C++, or Golang. Contributions to open-source projects or relevant technical publications. Compensation & Benefits
Compensation: The base salary range is $195,000 - $298,000 per year. Actual base compensation will vary based on factors relevant to the position. The information is a good faith estimate and may not apply outside certain locations. Bonus Potential: An incentive pay program based on company, job level, and individual performance. Benefits: Health, dental, vision, HSA/FSA, retirement savings, paid leave, and additional benefit programs. About GM
General Motors envisions a future with Zero Crashes, Zero Emissions, and Zero Congestion, and strives to lead change that makes the world better, safer, and more equitable for all. EEO & Accommodations
GM is an equal opportunity employer. All employment decisions are made without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, veteran status, or any other protected status in accordance with laws. If you require accommodations during your job search or application, please contact us via email or phone as listed in the posting. #J-18808-Ljbffr