About this role
Machine Learning Scientist/Engineer is a full-time role based in Sunnyvale, California at Wayve, suiting candidates with a strong research track record in multimodal foundation models and scalable training systems. The position involves designing foundation models for general-purpose robots within the Multi-Embodiment Generalist Agent team.
Role Overview
The Machine Learning Scientist/Engineer role at Wayve is located in Sunnyvale, California. The position is part of the Multi-Embodiment Generalist Agent team within Wayve Science, focusing on building foundation models for general-purpose robots that generalize across tasks and embodiments.
Key Responsibilities
Requirements
Skills and Tools
Salary and Benefits
About the Company
Wayve builds embodied intelligence that moves real vehicles safely and creates the AI platform for autonomous driving. The company pioneers an end-to-end AI approach enabling vehicles to learn directly from real-world experience, scaling from L2 plus through to L4 automation across global OEM partnerships. Wayve operates main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg using a hybrid working model.
How to Apply
To apply for this position, please visit the official company careers page.
Skills and tech stack
Questions candidates ask
Where is this role located?
This role is located in Sunnyvale, California USA, with a hybrid working model combining in-office collaboration and remote work.
What is the salary and equity offered?
The salary ranges from $311,450 to $419,760 and includes offers of equity.
What kind of experience is required for this position?
Candidates need experience in machine learning focusing on multimodal foundation models, scalable training, a strong research track record with top-tier publications, and strong engineering skills with machine learning frameworks.
Is visa sponsorship available?
Yes, visa sponsorship and relocation support are available where applicable.
What does the interview process involve?
The process includes an initial recruiter screen, competency interviews including PyTorch debugging and a hiring manager interview, deep-dive technical interviews covering systems and domain-specific topics, and a final mission and values alignment interview.
