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.
• Derive practical, deployable solutions and see them deployed on real-world vehicles

About You

You have deep expertise and prior experience in some or many of the following areas:

• Education: You have an M.Sc. or Ph.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.
• Technical Skills: You have strong problem solving and programming skills in Python (required) and C++ (beneficial) and ML frameworks such as PyTorch.
• Collaboration: Strong culture fit and good team player.
• Experience: You have 2+ years of deploying machine learning systems onboard, ideally in the area of prediction.
• Publications: Demonstrated research publications in top conferences (e.g. NeurIPS, ICLR, ICML, CVPR, RSS, CoRL, ICRA, IROS etc.)

Nice to have: Deep background in Embodied AI for robotics, Causal reasoning, Model interpretability and explainability, Joint prediction and planning, Understanding of Diffusion Models.

At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $193,930 and $291,150 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package.

At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #LI-DNP