To help facilitate administration of relocation benefits if you are selected, please apply using the permanent address you would move from.
Work Arrangement: Hybrid. This internship is categorized as hybrid. The selected intern is expected to report to the office up to three times per week or as determined by the team.
Locations:
As an intern on the trajectory generation team, you would work on well-scoped research or engineering projects that contribute to the team's ML-driven planning stack. This might involve implementing and benchmarking new neural network architectures for behavior prediction, improving data pipelines for training trajectory models, or conducting ablation studies on different learning approaches. The intern would collaborate closely with senior engineers and researchers, participating in code reviews, team meetings, and design discussions while gaining hands-on experience with production autonomous driving systems. They'd work with real-world driving data, simulation environments, and potentially see their contributions deployed to test vehicles.
The role offers a unique opportunity to bridge cutting-edge ML research with safety-critical robotics applications. An intern would develop skills in deep learning frameworks (PyTorch), work with large-scale distributed training systems, and learn how to validate and verify learned models for autonomous driving. Beyond technical skills, they'd gain insight into the challenges of building reliable AI systems for the real world—handling edge cases, ensuring safety guarantees, and balancing model performance with computational constraints. The experience provides exposure to how ML teams operate within a complex, multi-disciplinary engineering organization building toward full autonomy.
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