Staff ML Performance Engineer (Inference Optimisation)

Staff ML Performance Engineer (Inference Optimisation)

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.

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 Staff ML Performance Engineer, you’ll play a key role in high-impact projects, optimising ML inference for edge accelerators and GPUs. The focus of this team is to run 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) 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 both personally and professionally. The company fosters a culture of continuous learning and growth, providing opportunities for staff to engage in high-impact projects while contributing to groundbreaking advancements in automated driving systems.

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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)

Tip Number 1

Network like a pro! Reach out to folks in the industry, attend meetups, and connect with Wayve employees on LinkedIn. A personal touch can make all the difference when it comes to landing that interview.

Tip Number 2

Prepare for those technical interviews by brushing up on your skills. Dive into ML inference optimisation, compilers, and edge devices. Practising coding challenges and system design questions will help you shine during the interview.

Tip Number 3

Showcase your passion for self-driving tech! During interviews, share your thoughts on the future of AI in driving and how you can contribute to Wayve's mission. Your enthusiasm can set you apart from other candidates.

Tip Number 4

Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. 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)

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 optimised ML inference or improved performance in past projects. We love seeing real-world applications of your expertise.

Be Clear and Concise:Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s relevant. Make it easy for us to see why you’re a great fit for the role.

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 and shows your enthusiasm for joining our team at Wayve!

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 like TensorRT, CUDA, or Qualcomm QNN. Brush up on how these tools can optimise ML inference, as you'll likely be asked to discuss your experience with them during the interview.

Showcase Problem-Solving Skills

Prepare to discuss specific examples where you've identified and resolved performance bottlenecks in production systems. Highlight your approach to tackling tight constraints like latency and memory, as this will demonstrate your hands-on experience and critical thinking.

Communicate Clearly

Practice articulating complex technical concepts in a way that’s easy to understand. Being a clear communicator is essential, especially when aligning multiple stakeholders on performance trade-offs. Consider doing mock interviews to refine your delivery.

Emphasise Collaboration

Wayve values teamwork, so be ready to share experiences where you’ve collaborated with others, particularly model developers. Discuss how you influenced architectural decisions and contributed to team goals, showcasing your ability to work effectively in a fast-paced environment.