GM is looking to solve the problem of scaling its internal ML platform, building automation and self-service tools, and ensuring the reliability and efficiency of large-scale ML pipelines across the company, specifically through the development and evolution of AI Lineage
Requirements
- Strong programming skills in Python, C++, Go, or similar languages, with demonstrated experience building production-grade systems
- Hands-on experience working with relational and NoSQL databases
- Proven ability to design, build, and maintain highly scalable systems in production environments
- Experience with GCP, Azure, or AWS cloud platforms
- Familiarity with open-source ML orchestration tools such as Kubeflow, Flyte, Airflow, or similar platforms
- Experience with Kubernetes and container orchestration at scale
- Understanding of ML pipelines, data lineage, model lifecycle management, and reproducibility challenges in machine learning systems
Responsibilities
- Architect, implement, and test scalable, cloud-native distributed systems using modern cloud platforms such as Google Cloud Platform (GCP) or Microsoft Azure
- Lead technical projects end-to-end—from early design through production deployment
- Actively participate in design reviews, team planning, and code reviews
- Collaborate across multiple engineering teams to deliver cohesive platform solutions
- Foster a culture of technical excellence and growth
- Interview candidates using calibrated evaluation criteria, onboard new hires, and mentor engineers and interns to help them grow technically and professionally
- Design lineage tracking for data transformations, model training, evaluation runs, and pipeline dependencies
Other
- 8+ years of industry experience, with a strong focus on large-scale distributed systems or cloud infrastructure
- 3+ years of experience leading and delivering complex technical initiatives across teams
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Physics, or a related field—or equivalent practical experience
- Deep attention to detail, strong problem-solving skills, and a track record of building high-quality systems
- Passion for autonomous vehicles, infrastructure engineering, and advancing the state of ML platforms
