ML Research Scientist, Prediction & Smart Agents

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

The mandate of the prediction team is to use advanced machine learning techniques to improve the behavior of the Nuro Driver.

As a key member of the Prediction and Smart Agents team, you will focus on building state-of-the-art models for predicting the behavior of surrounding traffic. These models are crucial for our autonomous system, as they will be deployed onboard as part of our planning stack and used offboard for realistic closed-loop simulation.

You will explore novel machine learning methods to solve challenging real-world problems in autonomous driving. This work includes using generative sequence modeling approaches for robustly predicting complex, interactive traffic situations. It requires deep reasoning about the intentions of other road users and how their behaviors influence safe and correct driving decisions. You will also use different input modalities, including End-to-End (E2E) approaches, for predicting other agents. A vital component of this role is building smart, controllable agents to enable effective closed-loop training in simulation.

If you are passionate about solving challenging new problems, leading impactful research, and seeing your work deployed onto real robots, we encourage you to apply!

About the Work

• Design and build scalable, machine learning-based prediction systems to generate multi-modal, realistic, and kinematically feasible trajectories.
• Conduct cutting-edge research in generative sequence modeling and sequential decision-making. Areas of interest include, but are not limited to:
• Scalable generative sequence modeling approaches.
• Marginal, conditional, and joint distribution modeling for interactive agents.
• Transformer-based encoder-decoder architectures.
• Large generative models and diffusion models.
• Controllability of agents via conditioning, guidance, and other techniques.
• Collaborate closely with the Planning team to design realistic and controllable agents for closed-loop simulation, enabling agent training via Reinforcement Learning (RL).
• Mitigate accumulated uncertainties across interconnected autonomy components.
• Collaborate across various autonomy teams to develop holistic solutions for top challenges, proposing ideas, prioritizing.
• Derive practical, deployable so