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Staff / Senior Machine Learning Engineer, Reinforcement Learning

wayveSunnyvale, California USAAV Engineering
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Salary
Not listed
Job type
Full-time
Work mode
Remote
Experience
Senior
Posted
5 Oct 2026
Status
Open · checked 7d ago

About this role

Staff / Senior Machine Learning Engineer, Reinforcement Learning is a full-time position at Wayve in Sunnyvale, California, that suits experienced engineers focused on advancing reinforcement learning for autonomous driving systems. This role involves developing learning methods, leading technical direction for safety-critical models, and scaling reinforcement learning workflows to improve vehicle behavior.

Role Overview

Staff / Senior Machine Learning Engineer, Reinforcement Learning is an engineering role based in Sunnyvale, California, within Wayve's AV Core organisation. The position operates on a hybrid working model, combining in-person office collaboration with remote flexibility. The professional in this role works on developing learning methods that turn driving data into closed-loop behavior, technical ownership for reinforcement learning, and leading a learned emergency trajectory model for low-frequency, high-consequence maneuvers.

Key Responsibilities

Shape and execute the reinforcement learning roadmap for Driving Core and Core Model Safety against clear behavioral gaps and measurable success criteria.
Develop and evaluate post-behavior-cloning optimization methods, offline and off-policy reinforcement learning, and reward-guided approaches.
Improve reward models and learning signals used to train and evaluate driving policies.
Build robust training and experimentation workflows using large-scale driving data while diagnosing distribution shift, objective misspecification, optimization instability, and data or evaluation bias.
Define evidence across offline metrics, open-loop tests, closed-loop simulation, and on-road evaluation to distinguish policy improvement from benchmark overfitting.
Productionize successful methods in the shared ML stack, communicate decisions clearly, and raise the technical bar through design reviews, code reviews, and mentoring.

Requirements

Strong track record developing and experimentally validating reinforcement learning or sequential decision-making methods on complex, high-dimensional problems.
Deep understanding of modern reinforcement learning fundamentals including policy and value learning, off-policy learning, function approximation, distribution shift, and failure modes of learned objectives.
Hands-on experience with behavior cloning, reinforcement learning, or related methods.
Proficiency in Python and PyTorch with strong software engineering practices and experience building reliable machine learning training and evaluation systems.
Excellent experimental judgment to turn ambiguous behavioral problems into falsifiable hypotheses, useful metrics, disciplined ablations, and clear technical decisions.
Senior-level ownership and collaboration skills to lead a substantial technical area across research and engineering boundaries.

Nice to Have

Experience with offline reinforcement learning, imitation learning, reward modeling, preference learning, or post-training of large neural policies.
Experience in autonomous vehicles, robotics, control, or safety-critical physical systems.
Experience with closed-loop simulation, off-policy evaluation, uncertainty or calibration, and evaluation under rare or shifted conditions.
Experience training multimodal, transformer-based, or generative policy models at scale.
Proficiency in C++, CUDA, distributed training, or performance optimization for production machine learning systems.

Skills and Tools

Python
PyTorch
C++
CUDA
Reinforcement Learning
Machine Learning

Salary and Benefits

Salaries benchmarked against the market annually
Meaningful equity sharing in the ownership and long-term success of the company
Relocation support and visa sponsorship where applicable
Hybrid working and core hours
Learning and development budgets for training, conferences, and growth
Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, and wellbeing partnerships

About the Company

Wayve builds embodied intelligence to move real vehicles safely and creates an ecosystem for machines. The company pioneers an end-to-end AI approach that enables vehicles to learn directly from real-world experience, developing the ability to adapt, generalize, and improve at scale without relying on hand-coded rules or pre-mapped environments.

How to Apply

To apply for this position, please submit the application on the official company careers page.

Skills and tech stack

PythonPyTorchC++CUDAReinforcement LearningMachine Learning

Questions candidates ask

Where is this job located?

This role is based in Sunnyvale, California USA, with a hybrid working model that combines office collaboration and remote work.

What programming languages and frameworks are required?

Candidates must be proficient in Python and PyTorch.

What is the interview process like?

The process includes a 30-minute recruiter screen, 2 hours of competency interviews, 3 hours of deep-dive technical interviews, and a 45-minute final interview focused on mission and values alignment.

Is visa sponsorship available for this role?

Yes, relocation support and visa sponsorship are provided where applicable.

What core areas does the reinforcement learning engineer focus on?

The role focuses on advancing reinforcement learning methods for end-to-end driving models, executing the reinforcement learning roadmap, and leading a learned emergency trajectory model for safe, driverless operation.

Ready to apply?

The application is on the official wayve careers page.

Apply on company site