WA
Tech Lead, ML Engineer - AV Product engineering
WayveSunnyvalePosted 23 April 2026
Job Description
At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.
About us
Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.
Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.
In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.
At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.
Make Wayve the experience that defines your career!
The role
As a Tech Lead, Machine Learning Engineer within Wayve’s AV Product Engineering team, you will lead the navigation workstream for our end-to-end autonomous driving system - spanning L2+, L3, and robotaxi products. This is a rare opportunity to own your work from model training all the way through to deployment in production vehicles, with full visibility into the entire pipeline. Joining a small, high-impact team, you will set the technical direction for navigation ML and have meaningful scope for growth as the team expands.
Key Responsibilities
Lead the navigation workstream, including route planning, rerouting, and driving across L2+, L3, and robotaxi products.
Train and deploy end-to-end models for navigation and driving features, owning the full lifecycle from model training through to vehicle integration and production deployment.
Define the roadmap and technical vision for navigation ML within the AV Features team, helping shape the direction of L2+ driving features.
Collaborate closely with the Evaluation, Robot Software, and Data Platform teams to iterate rapidly and improve model performance.
Leverage closed-loop and open-loop evaluation frameworks to measure driving quality and validate production readiness.
Mentor and support junior engineers on the team and shape the long-term technical direction
About you
In order to set you up for success as a Tech Lead ML Engineer at Wayve, we’re looking for the following skills and experience
Essential
7+ years of ML engineering experience with a strong track record of shipping deep learning systems to production.
Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.
Hands-on experience with transformer-based and multimodal architectures, including vision-language models (VLM), vision-language-action models (VLA), or equivalent.
Demonstrated ability to train and deploy end-to-end ML models for production systems.
Strong understanding of end-to-end learning approaches for driving, embodied AI, or related domains.
Ability to take full ownership of a technical workstream - driving it from research and experimentation through to production deployment.
Desirable
Prior work in autonomous driving, imitation learning, or trajectory prediction. Or background in the AV industry, ideally from a perception, planning, controls, or evaluation team.
Experience with closed-loop simulation and open-loop evaluation for a ... (truncated, view full listing at source)
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