Build tools for sampling, selecting, and discarding synthetic driving data with testable hypotheses
Train and evaluate models with mixtures of synthetic and logged data
Present and write about methods for synthetic data generation and training

🎯 Requirements

Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering or
Familiarity with methods of sampling from large models
Experience with post-training methods such as SFT, RL, and preferences
Modern deep learning frameworks such as PyTorch and Jax

🎁 Benefits

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

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