Staff/Lead Machine Learning Engineer, Behavior & Planning

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

We are looking for a Senior/Staff Machine Learning Engineer to be a technical leader on Nuro’s Behavior & Planning team. Our team owns how the Nuro Driver behaves on the road: prediction, decision making, and planning, and is responsible for turning Nuro’s large-scale driving data into safe, comfortable, and natural driving behavior.

In this role you will bring strong, general machine learning expertise to some of the hardest problems in autonomy and drive them from research through to deployment on real vehicles. You’ll work at the frontier of applied ML spanning areas such as foundation and world models, LLM/VLM reasoning, reinforcement and imitation learning, generative and diffusion models, and transformer-based prediction and planning.  You’ll apply this knowledge and experience to make our driving generalize as we scale across new geographies as we expand throughout the U.S. and globally, and across new vehicle platforms including robotaxi, personally owned vehicles, and delivery/logistics.

You will help set technical direction, lead initiatives that span multiple teams, and operate with autonomy.  This role will report to a Technical Lead Manager in Behavior. If you love solving hard new problems and seeing your models drive real robots in the physical world, come join us!

About the Work

• Technical Leadership: Lead ML initiatives across the autonomy stack framing ambiguous problems, setting technical direction, and driving them from idea to on-road deployment.
• Applied ML: Design, train, and productionize state-of-the-art models across some or many of: foundation and world models, LLM/VLM reasoning, reinforcement and imitation learning, generative and diffusion models, and transformer-based prediction and planning.
• Generalization & Scale: Build behavior and planning models that generalize to new cities and geographies (U.S. and global) and adapt to new vehicle platforms.
• Cross-Functional Collaboration: Partner closely with Perception, Simulation & Evaluation, and ML Infra & Data, as well as the broader Autonomy and Research orgs, to develop holistic solutions to top autonomy challenges, align on priorities, and unblock shared initiatives.
• Mentorship & Influence: Raise the technical bar of the team, mentor engineers and researchers, and help shape roadmaps and technical strategy beyond your immediate scope.

About You

Required Qualifications

• 7+ years building and deploying machine learning systems, with a track record of leading complex, multi-team technical initiatives.
• M.S. or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Robotics, or a related field or equivalent practical experience.
• Strong, general machine learning foundations. You can reason from first principles across model architectures, training, and evaluation, and clearly explain and apply state-of-the-art techniques. Depth in some of: sequential decision making, prediction, generative modeling, foundation/world models, or representation learning.
• Understanding of the full ML development cycle, from data collection and training to deployment, onboard inference considerations, and data iteration loops.
• Strong problem-solving and programming skills in Python (required) and/or C++.
• Demonstrated ability to lead initiatives, collaborate across many different teams, and operate independently with minimal oversight.
• Efficient and clear communication skills are a must, with the ability to align cross-functional stakeholders.

Preferred Qualifications

• Strong preference for prior experience in autonomous vehicles or robotics, and familiarity with how ML fits into a complete autonomy/planning stack, though we hire strong general-ML talent from adjacent domains and value the ability to ramp on a new domain quickly.
• Experience mentoring engineers or leading a team.  Interest in growing into a management or tech-lead track is not required but those paths may exist for strong performers.
• Familiarity with at least one major ML framework (PyTorch, JAX, TensorFlow).
• Research contributions in top venues (e.g., NeurIPS, ICLR, ICML, CVPR, RSS, CoRL) are a plus, but a track record of applied and production impact matters more.

  At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $235,030 and $352,290 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.