ML Research Scientist, Prediction & Smart Agents

Who We Are

Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets.

Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T.

Rowe Price, and other leading investors.

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. Sc. D.

(preferable) focusing on one or more of the following areas: Computer Science, Artificial Intelligence, Mathematics, or a closely related field Expertise: Subject matter expertise and research experience in one or more of the following: sequential decision-making, prediction, Imitation Learning, Deep Reinforcement Learning, generative modeling, large models (pretraining/finetuning), or machine learning for robotics.