General Motors (GM) is driving the future of mobility with advanced self-driving and electric vehicle technologies. The AV ML Infra team at GM builds ML infrastructure designed to meet the unique demands of AI and ML innovation, supporting a wide range of use cases across teams such as Embodied AI, Simulation, Data Science, and more. They enable scalable and efficient ML experimentation, enhance the productivity of ML engineers, and drive the adoption of cutting-edge ML techniques to tackle the complexities of autonomous driving technology and expedite the path to commercialization.
Requirements
- Proficiency in building scalable infrastructure on the cloud using Python, C++, Golang, or similar languages.
- Experience working with relational and NoSQL databases.
- Demonstrated ability to develop and maintain systems at scale.
- Experience with Google Cloud Platform, Microsoft Azure, or Amazon Web Services.
- Experience with open-source orchestration platforms such as Kubeflow, Flyte, Airflow, etc.
- Experience with Kubernetes.
- Understanding of Machine Learning (ML) models/pipelines.
Responsibilities
- Utilize the latest cloud technologies (GCP/Azure) to design, implement, and test scalable distributed computing and data processing solutions in the cloud.
- Take ownership of technical projects from inception to completion, contribute to the product roadmap, and make informed decisions on major technical trade-offs.
- Engage effectively in team planning, code reviews, and design discussions, considering the impact of projects across multiple teams while proactively managing conflicts.
- Conduct technical interviews with calibrated standards, onboard, and mentor engineers and interns, fostering a culture of growth and knowledge sharing.
- Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines.
- Streamlines and optimizes large-scale ML training and inference across cloud and on-prem compute resources.
- Automates ML workflows while tracking data and model lineage across diverse infrastructures, accelerating engineering velocity and ensuring reproducibility.
Other
- 5 + years of experience, with a strong background in large-scale distributed systems preferred.
- 1 + years of experience leading and driving large-scale initiatives.
- A Bachelor’s, Master’s, or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Physics, or a related field; or equivalent practical experience.
- A passion for autonomous vehicle technology and its transformative potential.
- A startup mentality with a willingness to embrace uncertainty and wear multiple hats.
