You will:- Build tools for sampling, selecting, and discarding synthetic driving data with testable hypotheses of how that data will lead to improvement
- Train and evaluate models with mixtures of synthetic and logged data
- Present and write about methods for synthetic data generation and training
You have:- Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering or a related technical field
- 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
We prefer:- Experience working with open and closed loop decision making models in robotics
- Experience with synthetic data generation and evaluation
- Prior projects using LLM-as-a-judge
Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in.
The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company's generous benefits programs, subject to eligibility requirements.
Hourly PhD Pay
$85-$85 USD
