Applied Scientist Machine Learning Engineer

1 hour agoSeniorSalary: 312K - 370KSunnyvale, California USA Hybrid Full Time Ai Jobs by Wayve

Wayve

Wayve is a London-headquartered embodied-AI company developing and licensing mapless, vehicle-agnostic driving software for assisted, automated, and robotaxi applications.

Series DRecently fundedCompany intelligence

London, United Kingdom

Funding history

Employee tender offerSeries D extension ($60M)Series D ($1.2B)

Investors

Rosemary Leith Uber Nvidia Baillie Gifford SoftBank Vision Fund 2

About Wayve

Wayve Technologies Ltd. develops the Wayve AI Driver, an end-to-end, data-trained software platform that runs on onboard vehicle compute and native sensors. It is designed for OEM integration across L1 driver assistance through L4 automated driving, without HD maps.

View jobs by Wayve

Skills

Active Learning Data Curation Data Enrichment Data Quality Deduplication Embedding Evaluation Foundation Model Labeling Machine Learning Python Pytorch Reinforcement Learning Reward Modeling Simulation Vision-Language-Action World Model

Candidate Availability

Hybrid · Sunnyvale · Required

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will curate fleet data, build enrichment and labeling pipelines, train and fine-tune large-scale models, and design offline and closed-loop evaluation. You will develop methods for embodied vision-language-action models, world models, policy learning, reinforcement learning, and reward modeling.

Requirements

Master’s degree with around 6 or more years of relevant experience, or PhD with 2 or more years
Machine learning and software fundamentals
Experience taking machine learning research into production systems at scale
Experience in data curation, foundation-model training, large-scale data wrangling, or foundation-model evaluation
Experience with large-scale data or large neural networks
Python proficiency
Experience with PyTorch or a similar deep-learning framework

Responsibilities

Mine fleet data for rare, long-tail, and safety-critical events
Develop repeatable training-data curation across cities, sensor rigs, and embodiments
Build automated and semi-automated enrichment, labeling, and data-quality pipelines
Build and fine-tune large-scale pretrained models
Develop embodied vision-language-action models for driving
Design offline and closed-loop evaluation metrics and benchmarks
Use world-model-based evaluation for counterfactual scenarios
Contribute to policy learning, reinforcement learning, and reward modeling

Benefits

Hybrid working
Competitive equity package