Staff ML Performance Engineer (Inference Optimisation) in London

Staff ML Performance Engineer (Inference Optimisation) in London

London Full-Time 70000 - 90000 £ / year (est.) No working from home possible
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At a Glance

  • Tasks: Optimise ML inference for edge devices and contribute to groundbreaking AI projects.
  • Company: Wayve, a leader in Embodied AI technology for automated driving.
  • Benefits: Hybrid working policy, inclusive culture, and opportunities for professional growth.
  • Other info: Dynamic environment with a focus on collaboration and innovation.
  • Why this job: Join a team tackling complex challenges in the exciting world of self-driving cars.
  • Qualifications: Experience in performance optimisation and strong software engineering skills required.

The predicted salary is between 70000 - 90000 £ per year.

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.

The role involves optimising ML inference for edge accelerators and GPUs, focusing on running large transformer-based models efficiently on low-cost, low-power edge devices to enable Wayve’s first driving product. You’ll help set the technical direction for turning these models into production systems that run reliably on in-vehicle compute. This is a hands-on role working across ML systems, compilers, runtimes, kernels, and embedded deployment, contributing to several early-stage, high-impact projects at Wayve.

Key responsibilities:

  • Profile and pinpoint bottlenecks across the full inference stack (model graph, compiler/runtime, kernel execution, memory movement) and deliver measurable improvements.
  • Implement and validate optimisations in compilers, runtimes, and/or kernels (e.g. operator fusion, scheduling, quantisation‑aware performance, custom kernels).
  • Build robust benchmarking and regression testing to ensure performance improvements hold across models, devices, and software releases.
  • Optimise for multiple targets (e.g. NVIDIA Orin/Thor, Qualcomm) and work with teams to support these in a maintainable way.
  • Collaborate with model developers to influence architecture and training/deployment decisions that affect on‑device performance.
  • Contribute to technical roadmaps and tooling and help raise the standard of performance engineering across the team.

About you

Essential:

  • Proven experience improving performance in production systems with tight constraints (latency, memory, bandwidth, power/thermal, or cost).
  • Strong proficiency with at least one relevant stack/toolchain (e.g. TensorRT, CUDA, Qualcomm QNN, Triton, OpenCL) and confidence learning adjacent frameworks quickly.
  • Comfort operating at multiple levels of abstraction — from high‑level model behaviour down to low‑level kernel/runtime execution.
  • Strong software engineering fundamentals (debugging, profiling, testing, and maintainable code).
  • Clear communicator and collaborative teammate; able to align multiple stakeholders on performance trade‑offs and priorities.

Desirable:

  • Exposure to embedded or edge deployment of ML models, including benchmarking on real devices and handling system‑level constraints.
  • Experience with NVIDIA and/or Qualcomm SoCs and performance tooling.
  • Python and C++ proficiency.
  • Experience mentoring others and/or driving technical direction in a small, fast‑moving team.

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.

Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. 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.

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.

Staff ML Performance Engineer (Inference Optimisation) in London employer: Icehouseventures

Wayve is an exceptional employer that champions innovation and inclusivity in the rapidly evolving field of AI technology. With a hybrid working policy that balances collaborative office time in London with flexible remote work, employees are empowered to thrive in a supportive environment that values diverse perspectives and fosters professional growth. Join us to make a meaningful impact on the future of automated driving while being part of a team that embraces challenges and celebrates achievements.

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Contact Details:

Icehouseventures Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Staff ML Performance Engineer (Inference Optimisation) in London

Tip Number 1

Network like a pro! Reach out to folks in the industry, attend meetups, and connect with Wayve employees on LinkedIn. A friendly chat can sometimes lead to opportunities that aren’t even advertised!

Tip Number 2

Prepare for those interviews! Brush up on your technical skills related to ML performance engineering. Practice explaining complex concepts simply, as clear communication is key when collaborating with teams.

Tip Number 3

Show off your passion for AI and self-driving tech! During interviews, share your thoughts on the future of autonomous driving and how you can contribute to Wayve’s mission. Enthusiasm goes a long way!

Tip Number 4

Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in being part of the Wayve team.

We think you need these skills to ace Staff ML Performance Engineer (Inference Optimisation) in London

ML Inference Optimisation
Performance Engineering
TensorRT
CUDA
Qualcomm QNN
Triton
OpenCL

Some tips for your application 🫡

Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience with ML performance engineering. Use keywords from the job description to show that you understand what we're looking for.

Showcase Your Skills:Don’t just list your skills—demonstrate them! Include specific examples of how you've improved performance in production systems, especially under tight constraints. We want to see your impact!

Be Clear and Concise:When writing your application, keep it clear and to the point. Avoid jargon unless it's relevant, and make sure your passion for self-driving cars shines through. We love a good story!

Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows you're serious about joining our team!

How to prepare for a job interview at Icehouseventures

Know Your Tech Stack

Make sure you’re well-versed in the relevant stacks and toolchains mentioned in the job description, like TensorRT or CUDA. Brush up on your knowledge of how these tools can optimise ML inference, as you'll likely be asked to discuss specific examples from your experience.

Showcase Problem-Solving Skills

Prepare to discuss past projects where you identified and resolved performance bottlenecks. Be ready to explain your thought process and the measurable improvements you achieved, especially under tight constraints like latency or power.

Collaborate and Communicate

Since this role involves working with various teams, practice articulating your ideas clearly. Think of examples where you successfully aligned stakeholders on performance trade-offs, as strong communication is key in a collaborative environment.

Demonstrate Continuous Learning

Wayve values those who embrace learning and evolving. Share instances where you’ve quickly adapted to new frameworks or technologies, and express your enthusiasm for staying updated in the fast-paced world of AI and ML.