Machine Learning Research Scientist: Generative Modeling for Planning
Develops state-of-the-art generative models like diffusion and flow-matching for autonomous planning in self-driving tech. Requires PhD or MSc with 2-3 years experience in generative modeling for robotics, strong Python/C++ skills, and top research publications.
About the job
About the Work
- 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. Leverage 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. Explore joint prediction and planning and self-play. Leverage generative models for active learning and world modeling.
- Develop controllable generative models to guide the generation process towards desired goals, conditions and rewards.
- Collaborate across autonomy teams while developing holistic solutions to top autonomy challenges. Understand issues, propose ideas, prioritize work and develop solutions to solve them, evaluate your solution by deploying the models on to the NuroDriver.
About You
You have deep expertise and prior experience in some or many of the following areas:
- You have a Ph.D. (preferable) or M.Sc. with 2-3 years of experience working with generative models in the lab, in industry, or both.
- Research experiences in generative models, particularly diffusion models, flow matching and energy-based models, for robotics, including manipulation, path planning, and autonomous driving. Experiences in vision-language-action models, reinforcement learning for generative model optimization, video generation, text-to-image generation, diffusion models for LLMs, world foundation models, and other applications of generative modeling are a bonus!
- You have strong problem solving and programming skills in Python and/or C++
- Strong culture fit and good team player.
- Demonstrated research publications in top conferences (e.g. NeurIPS, ICLR, ICML, CVPR, RSS, CoRL etc.)
Compensation
Base pay range: $160,360 - $240,540/year. Eligible for annual performance bonus, equity, and competitive benefits package.
Skills
Diffusion Models, Flow-Matching, Energy-Based Models, Generative Models, Reinforcement Learning, LLMs, Python, C++, Foundation Models, Vision-Language-Action Models
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