Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Machine Learning Engineer, Inference Optimisation

Wayve
City of London
1 week ago
Create job alert
Overview

ML Compression Engineer - Inference Optimisation at Wayve. The role focuses on optimising the Driving Model to run efficiently in millions of vehicles using consumer-grade GPUs and accelerators. The team targets running large transformer-based models efficiently on low-cost, low-power edge devices to enable Wayve’s first driving product, using methods such as distillation, efficient architectures, pruning, quantisation and other techniques. This is an opportunity to own and lead high-impact, early-stage projects at Wayve with the goal of enabling product deployments on 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
  • 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.

Job details
  • Seniority level: Entry level
  • Employment type: Full-time
  • Job function: Engineering and Information Technology
  • Industries: Software Development

Referrals increase your chances of interviewing at Wayve by 2x

Get notified about new Machine Learning Engineer jobs in London, England, United Kingdom.


#J-18808-Ljbffr

Related Jobs

View all jobs

Staff Machine Learning Performance Engineer, Inference Optimisation

Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer - AI & GPU Performance

Principal Machine Learning Engineer

Senior Machine Learning Engineer (Large Systems)

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.