Senior AI​/ML Fullstack Engineer - AV ML Infra

About the team:

The AV ML Infra team at GM builds end-to-end ML platforms and developer-facing products 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. We enable scalable and efficient ML experimentation, enhance the productivity of ML engineers, and drive the adoption of cutting‑edge ML techniques.

Our ML infrastructure includes:

• AI Validation & Inference: Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines.
• ML Compute: Streamlines and optimizes large-scale ML training and inference across cloud and on-prem compute resources.
• AV Pipelines & Lineage: Automates ML workflows while tracking data and model lineage across diverse infrastructures, accelerating engineering velocity and ensuring reproducibility.

As a Senior AI/ML Full‑Stack Engineer
, you will design and build end-to-end software products, owning everything from user‑facing interfaces to backend services and cloud infrastructure. You will lead technically complex projects, collaborate closely with product managers and platform teams, and mentor junior engineers.

This is a hands‑on individual contributor role with a strong emphasis on technical depth, system design, and product impact rather than people management.

This role is part of an ML infrastructure engineering team and does not involve applying machine learning models for specific tasks. The focus is on developing infrastructure products that empower GM teams to perform machine learning and data science at scale.

• Full‑Stack Development: Design, implement, and maintain user‑facing web applications and internal tools used by AV engineering teams. Build scalable backend services and APIs that power frontend experiences. Ensure systems are secure, reliable, and performant at scale.
• Frontend Engineering: Develop modern, responsive UIs using frameworks such as React, Angular, or similar. Collaborate with UX and product partners to deliver intuitive workflows and polished user experiences. Translate complex backend concepts into clear, usable interfaces.
• Design & Implementation: Utilize the latest cloud technologies (GCP/Azure) to design, implement, and test scalable distributed computing and data processing solutions in the cloud.
• Project Ownership: Take ownership of technical projects from inception to completion, contribute to the product roadmap, and make informed decisions on major technical trade‑offs.
• Collaboration: Engage effectively in team planning, code reviews, and design discussions, consider the impact of projects across multiple teams while proactively managing conflicts.
• Mentorship & Recruitment: Conduct technical interviews with calibrated standards, onboard, and mentor engineers and interns, fostering a culture of growth and knowledge sharing.

• 5+ years of professional software engineering experience, including full‑stack development.
• Experience building and operating production‑grade web applications.
• 1+ year of experience leading and driving large‑scale initiatives.
• Proficiency in building scalable infrastructure on the cloud using Python, C++, Golang, or similar languages.
• Experience working with relational and No
  
  SQL databases.
  
  Demonstrated ability to develop and maintain systems at scale.
• 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.
• Strong attention to detail and a commitment to accuracy.
• A proven track record of efficiently solving complex problems.
• A startup mentality with a willingness to embrace uncertainty and wear multiple hats.

• 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.
• Python/C++/Golang proficiency.
• Relevant publications.