Machine Learning Engineer - Pre-Training

Wayve
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer / MLOps Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Join to apply for the Machine Learning Engineer - Pre-Training role at Wayve.


At Wayve, we are committed to creating a diverse, fair, and respectful culture that is inclusive of everyone regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis 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 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.


The Role

We are seeking skilled engineers to join our Training Tech team, working on optimizing large-scale training jobs to scale our models through the next order of magnitude. The successful candidate will increase the efficiency of training jobs to allow Wayve to train larger models faster.


Key Responsibilities

  • Profile training jobs to identify bottlenecks, e.g. using NVIDIA Nsight Systems.
  • Design and implement efficiency improvements to maximize MFU, e.g. tensor parallelism, model compilation, mixed precision.
  • Design and implement observability tools, e.g. to track MFU.
  • Collaborate closely with Research teams to integrate training efficiency improvements and create a culture of performance optimization.

About You

Essential qualifications and experience:



  • Experience optimizing large-scale training jobs on GPU compute clusters.
  • Experience working in platform teams and with research teams.
  • Experience reporting and tracking benchmarked performance over time in an open and accessible way.
  • Ability to write high-quality, well-structured, and tested Python code.
  • BS or MS in Machine Learning, Computer Science, Engineering, or a related technical discipline, or equivalent experience.

Desirable skills

  • Solid experience working with concurrent, parallel, and distributed computing.
  • Experience using NVIDIA Nsight Systems.
  • Experience implementing GPU kernels.
  • Knowledge of computing fundamentals—what makes code fast, secure, and reliable.

Location & Working Policy

This is a full-time role based in our London office. We operate a hybrid working policy that combines time together in our offices and workshops with time working from home. You can shape your schedule around core working hours while collaborating with a high-performing team.


We understand that not every applicant will meet all of the requirements listed above. If you’re passionate about self-driving cars and believe you can positively impact the world, we encourage you to apply.


For more information, visit Careers 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 capture information about care responsibilities, disabilities, and other diversity data through an optional DEI monitoring form to help improve our hiring process and ensure it is inclusive and non-discriminatory.


#J-18808-Ljbffr

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.