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 the Tech Lead, ML Edge Deployment you’ll lead the strategic direction for Wayve’s GPU kernel development. This role occupies a critical junction between machine learning and embedded systems, enabling us to deploy our transformer-based driving models efficiently onto autonomous vehicles. This is an exciting opportunity to lead in several high impact, early stage projects at Wayve with the ultimate goal of enabling product deployments onto millions of customer vehicles around the world.
Key Responsibilities
- You will lead a multi-disciplinary team of GPU kernel engineers, breaking down large milestones into objectives which can be delivered by yourself and the team
- As a hands-on engineer, you will deliver critical roadmap milestones in enabling efficient inference on multiple target GPUs and accelerators
- You will work closely with members of the ML and Software teams to optimise models for deployment on edge
- You will have opportunities to develop new skills, especially in model optimisation
About you
In order to set you up for success as a Tech Lead in ML Edge Deployment at Wayve, we’re looking for the following skills and experience.
Essential
- Proven experience as a technical lead or senior engineer on complex engineering projects
- Experience developing GPU kernels (e.g. CUDA, OpenCL, etc)
- Proficiency in C++ and ML frameworks such as PyTorch
- Excellent interpersonal and communication skills
- Ability to mentor and guide a team of engineers
Desirable:
- Experience with ML deployment pipelines
- Experience with embedded SoCs used in automotive environments, e.g. Nvidia, Qualcomm, Renesas, etc
This is a full-time role based in our office in London. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We operate core working hours so you can determine the schedule that works best for you and your team.
We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.
For more information visit Careers at Wayve.
To learn more about what drives us, visit Values at Wayve
DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.
