You will:- Literature research of SOTA in RSI, physics AI foundational models, autonomous driving foundational models, World Action models and connect the SOTA to their research projects to improve driver quality
- Design new data and post-training recipe to RL the driver with the critic model and world model in a close loop setup
- Coding and implementing the RSI system
- Inspect training results and ablation studies to iterate the algorithm design
You have:- Currently pursuing a PhD in Computer Science, Robotics, Machine Learning, or a related quantitative field
- Depth in foundational AI model post-training, especially RL
- Fluency in using AI coding agents, fluency in one of the RL training framework such as Rex, Verl, NeMo-RL
- Fluency in AI model training basics such as transformer architecture, hyperparameter tuning, overfitting debug
- Familiarity with autonomous driving, physics AI, VLA, or world models
We prefer:- Depth in RL for robotics or autonomous driving models
- Depth in world action models or video generation models
- Experience in Recursive Self-Improvement training for AI models.
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
