Machine Learning intern, Behavior Planning
- Work on scalable machine learning based planning and prediction systems to generate safe and feasible trajectories for autonomous driving.
- Collaborate closely within the learned behavior team analyze autonomy system performance, understand data quality and feature representations, and modify machine learning models to develop holistic solutions to top autonomy challenges,
- Implement and train machine learning models using established frameworks like PyTorch. Test and deploy your work on real-world vehicles.
- Conduct experiments, analyze results, and present findings to the team.
- (PhDs) Research generative sequence modeling and sequential decision making. Research backgrounds we are looking for but not limited to are:
- Scalable Imitation learning and reinforcement learning.
- Marginal, conditional, and joint prediction modeling for interactive agents.
- Large generative, diffusion, and VLA models.
- Learning from human preference feedback and expert demonstrations, reward modeling and optimization.
- Currently pursuing a B.S. or M.S. in Computer Science, Robotics, Mathematics, or a related engineering field.
- Excellent programming proficiency in Python and a strong understanding of object-oriented programming. Solid grasp of fundamental computer science concepts, including data structures, algorithms, and software design. Experience with C++ is a plus.
- Strong foundational knowledge of machine learning concepts (e.g., supervised/unsupervised learning, model evaluation). Hands-on experience with at least one major deep learning framework (PyTorch, TensorFlow, or JAX).
- Coursework or project experience in one or more of the following is highly desirable: Deep Learning, Computer Vision, Robotics, or Reinforcement Learning.
- Demonstrated knowledge in ML systems and infrastructure, with experience in training optimization, compiler acceleration, quantization, and efficient deployment of high-performance models is a strong plus.
- Demonstrated ability to build projects, shown through coursework, personal projects (e.g., on GitHub), or a previous internship.
- A collaborative, curious, and proactive mindset paired with strong technical communication skills. Eager to learn and contribute as a team player.
- Currently pursuing a Ph.D. in Computer Science, Artificial Intelligence, Robotics, or a closely related field.
- A clear research focus in one or more of the following areas: generative and/or diffusion modeling, sequential decision making, Imitation Learning, Deep Reinforcement Learning, large models (pre-training/fine-tuning), or machine learning for robotics.
- A deep understanding of the theoretical underpinnings of modern machine learning.
- Strong programming skills in Python for rapid prototyping and experimentation with complex ML models. Experience with C++ is a plus.
- A track record of research, with publications in top-tier conferences (e.g., NeurIPS, ICLR, ICML, CVPR, RSS, CoRL)
- Intellectual curiosity, attention to detail, persistence, and the ability to work both independently and collaboratively within a research-oriented team paired with strong technical communication skills.
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