The opportunity
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…
What you'll do
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.
+ years building and deploying machine learning systems, with a track record: of leading complex, multi-team technical initiatives.
What they're looking for
- 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++.
