Staff/Lead Machine Learning Engineer, Behavior & Planning

Nuro•Mountain View, CA

3d•$235,030 - $352,290

About The Position

Nuro is seeking a Staff Machine Learning Engineer to serve as a technical leader within the Behavior & Planning team. This team is responsible for the Nuro Driver's on-road behavior, encompassing prediction, decision-making, and planning. Their objective is to transform Nuro's extensive driving data into safe, comfortable, and natural driving actions. The role involves applying strong, general machine learning expertise to complex autonomy challenges, guiding them from research to deployment on actual vehicles. The engineer will work at the forefront of applied ML, exploring areas like foundation and world models, LLM/VLM reasoning, reinforcement and imitation learning, generative and diffusion models, and transformer-based prediction and planning. This knowledge will be used to enhance the driving system's generalization capabilities as Nuro expands into new geographical regions and across different vehicle platforms, including robotaxis, personal vehicles, and delivery/logistics vehicles. This is a high-ownership position where the individual will define technical direction, lead cross-team initiatives, and operate with significant autonomy and minimal supervision. It presents a clear opportunity for growth into management or a tech-lead role as the team expands. The role is ideal for someone who enjoys solving difficult new problems and seeing their models operate real robots in the physical world.

Requirements

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. Ability to 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.

Nice To Haves

Strong preference for prior experience in autonomous vehicles or robotics, and familiarity with how ML fits into a complete autonomy/planning stack.
Experience mentoring engineers or leading a team, and interest in growing into a management or tech-lead track.
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 strong track record of applied and production impact matters more.

Responsibilities

Lead ML initiatives across the autonomy stack framing ambiguous problems, setting technical direction, and driving them from idea to on-road deployment with minimal oversight.
Design, train, and productionize state-of-the-art models across areas such as foundation and world models, LLM/VLM reasoning, reinforcement and imitation learning, generative and diffusion models, and transformer-based prediction and planning.
Build behavior and planning models that generalize to new cities and geographies (U.S. and global) and adapt to new vehicle platforms.
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.
Own the full model lifecycle: data, training, onboard inference, closed-loop and open-loop evaluation, and continuous on-road iteration.
Raise the technical bar of the team, mentor engineers and researchers, and shape roadmap and technical strategy beyond your immediate scope.