Research, design, and implement novel deep learning architecture, multi-task loss formulations, and
Train and diagnose large-scale neural network using distributed TPU infrastructure, analyzing
Evaluate trained models across open-loop and closed-loop benchmarks, triaging real-world driving

🎯 Requirements

Currently pursuing a Ph.D. in Machine Learning or a related quantitative field.
Strong hands-on proficiency in Python with at least one modern deep learning framework (e.g., JAX
Solid mathematical foundation in deep learning, optimization, loss formulation and empirical model
Experience designing, running, and analyzing rigorous ML experiments on large-scale datasets.

🎁 Benefits

Interns are eligible to participate in the Company’s generous benefits programs, subject to

🛃 Visa sponsorship

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