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General Motors LLC is seeking a software-oriented intern to help build and optimize AV ML infrastructure in Sunnyvale, CA. You will improve data processing pipelines, scheduling, and model training/inference workflows while assisting with testing infrastructure like Carbench/HIL.
You will collaborate with engineers to develop scalable tools, instrument systems, and monitor performance, aiming to reduce latency and accelerate autonomous driving experiments.
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. Our AV 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 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.
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