You will:- Research and develop novel visual and spatio-temporal reasoning methods for multimodal Perception Foundation Models using AV sensor data
- Design and implement post-training recipes-including Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL)-to improve perception generalization in long-tail autonomous driving scenarios
- Conduct large-scale experiments and ablation studies on autonomous driving benchmarks, with the opportunity to publish findings at top-tier AI/CV conferences (e.g., CVPR/ICCV/ECCV/NeurIPS/ICLR/ICML)
You have:- Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering or a related technical field
- Hands-on experience & strong publications in perception, and/or LLM/VLM
- Knowledge of pre-training and post-training of the large foundation models
- Strong Python coding skills with expertise in modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow)
We prefer:- Experience with visual reasoning for LLM/VLM/robotics/driving
- Experience with RL training in LLM/VLM/robotics
- Experience with open-vocabulary perception
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
