Machine Learning Engineer, Inference Optimisation

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 Machine Learning Engineer for the ML Optimisation team, you’ll optimise our Driving Model to run efficiently in millions of vehicles with consumer-grade GPUs and accelerators. The focus of this team is to run large transformer-based models efficiently in low-cost, low-power edge devices to enable Wayve’s first driving product - e.g. via methods such as distillation, efficient architectures, pruning, quantisation and others. This is an exciting opportunity to own and lead 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:

• Develop state of the art techniques in distillation, quantisation, pruning and other ML compression methods to achieve latency, bandwidth and compute targets

• Understand how ML compression methods affect driving behaviour

• Stay up to date with latest papers, conferences etc

• You’ll collaborate closely with other model developers and scientists across the business to drive innovation and delivery

• You’ll have the opportunity to develop new skills and experience

About you

Essential

• 2+ years working as an MLE

• Experience working on optimisation problems with hard latency and/or resource constraints

• Strong engineering background

• Proficiency with PyTorch

• Excellent interpersonal and communication skills

Desirable

• Experience with ML on edge, e.g. automotive, drones, AR/VR, IoT, etc

• Experience with any of the following: quantisation, distillation, pruning, sparsity methods, NAS, efficient architectures, etc

• Experience with Nvidia and Qualcomm SoCs and frameworks are valuable, but not required

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.