General Motors
AI/ML Engineer Intern - Autonomous Vehicle ML Infrastructure
USA internshipsAI/ML/DataPosted 12 days ago
- Location
- Sunnyvale, CA
- Type
- USA internships
Apply nowOpens General Motors’s application page
General Motors
From General Motors’s careers page.
Hybrid: This internship is categorized as a hybrid. The selected intern is expected to report to the office up to three times per week or as determined by the team.
Sunnyvale, California
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. We enable scalable and efficient ML experimentation, enhance the productivity of ML engineers, and drive the adoption of cutting-edge ML techniques.
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 via Orchestration platform while tracking data and model lineage across diverse infrastructures, accelerating engineering velocity and ensuring reproducibility.
Together, these tools and systems empower GM to tackle the complexities of autonomous driving technology and expedite our path to commercialization.
As an intern, you will help develop and optimize our AV ML Infra by improving data processing pipelines, scheduling, accelerating model training and inference workflows, and/orenhancing testing infrastructure like Carbench/HIL. You will support tool development, instrumentation, and system monitoring to boost reliability, reduce latency, and increase iteration speed for autonomous driving performance.
regression, and rules-based models, operations, and inference.
roadmap.
environments (e.g. distributed clusters, multicore SMP, and GPU).
functional teams.
experience, coding competitions.
model analysis, or experiment planning).
students selected for the 2027 Student Program.





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