Senior/Staff Machine Learning Research Scientist: Generative Modeling for Planning

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

144% above the BLS national median for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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About this role

Senior/Staff Machine Learning Research Scientist: Generative Modeling for Planning Mountain View, California (HQ) 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 collaborate closely with researchers and engineers on the Learned Behavior teams to tackle plan generation challenges in autonomous driving. You'll apply state-of-the-art generative modeling techniques-ranging from cutting-edge diffusion, flow matching, energy-based models, and SoTA algorithms-in order to develop novel solutions that generate safe, comfortable, and efficient driving behaviors in the most challenging real world situations. Beyond core research, you'll own the end-to-end lifecycle of your models, productizing them for robust, real-world autonomous driving deployments on a global scale. 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

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