Machine Learning intern, Behavior Planning at Nuro LLC

Mountain View, California, United States -

Full Time

Start Date

Immediate

Expiry Date

29 Apr, 26

Salary

14000.0

Posted On

29 Jan, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Python, Reinforcement Learning, Deep Learning, Robotics, Computer Vision, C++, Data Structures, Algorithms, Software Design, Generative Modeling, Imitation Learning, Sequential Decision Making, Model Evaluation, Collaboration, Technical Communication

Industry

Software Development

Description

Who We Are Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale, empowering a safer, richer, and more connected future. About the Role In this role, you will be a key member of the behavior team focused on solving the hardest autonomy problems using advanced machine learning techniques. Here you will explore novel and advanced machine learning methods to solve practical real-world challenging problems in autonomous driving. This work may include using imitation and reinforcement learning to reason about high level decisions and generate executable motion plans; develop robust models to handle challenging interactive situations; reason about the intentions of other road users and how their behaviors influence safe and correct driving decisions; build end-to-end driving systems; work with complex real world datasets; and more. If you love solving challenging new problems by leading research and seeing it through to deployment onto real robots come join us! Internship candidates that are able to join for >=6 months are strongly preferred. About the Work 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. About You You have deep expertise and prior experience in some or many of the following areas: Undergraduate or Masters Students 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. Ph.D. Students 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. For this position, the reasonably expected monthly pay is between $11,500 and $14,000. Your actual base pay will depend on several factors, including your experience, qualifications, education, location, and skills. At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #LI-DNP

Responsibilities

You will work on scalable machine learning-based planning and prediction systems to generate safe and feasible trajectories for autonomous driving. This includes collaborating with the learned behavior team to analyze system performance and modify machine learning models to address autonomy challenges.