Machine Learning Research Scientist: Generative Modeling for Planning

• Develop and scale state-of-the-art generative models—especially diffusion architectures, flow-matching techniques, and energy-based models—for autonomous plan generation.
• Build generative models with foundation models, leveraging large language models and world foundation models for reasoning, decision making and multi-modality generation.
• Optimize generative models using reinforcement learning to improve interactive reasoning, explore reward modeling/learned verifier using generative models, and develop controllable generative models to guide generation towards desired goals.
• Collaborate across autonomy teams to develop holistic solutions to top autonomy challenges and deploy models onto the NuroDriver.

This role contributes to making autonomy accessible to all by advancing generative modeling for planning, which helps build scalable self-driving technology that empowers a safer, richer, and more connected future.

• Ph.D. (preferable) or M.Sc. with 2-3 years of experience working with generative models in lab, industry, or both.
• Research experience in generative models (diffusion models, flow matching, energy-based models) for robotics, autonomous driving, or related fields; bonus for vision-language-action models, reinforcement learning, video generation, etc.
• Strong problem solving and programming skills in Python and/or C++.
• Demonstrated research publications in top conferences (e.g., NeurIPS, ICLR, ICML, CVPR, RSS, CoRL).

Base pay range of $160,360 to $240,540/year, plus annual performance bonus, equity, and a competitive benefits package. Nuro fosters a diverse and inclusive workplace with psychological safety for all employees.

Nuro is a self-driving technology company founded in 2016, building the world's most scalable driver by combining cutting-edge AI with automotive-grade hardware to license its core technology, the Nuro Driver™, for applications like robotaxis and commercial fleets.