At a Glance
- Tasks: Optimise ML inference for edge devices and contribute to high-impact projects.
- Company: Join Wayve, a leader in self-driving technology with a collaborative spirit.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with opportunities to mentor and lead technical direction.
- Why this job: Make a real impact on the future of self-driving cars while working with cutting-edge tech.
- Qualifications: Experience in performance optimisation and strong software engineering skills required.
The predicted salary is between 60000 - 80000 £ per year.
Requirements
- 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
- (Desirable) Experience with NVIDIA and/or Qualcomm SoCs and performance tooling
- (Desirable) Python and C++ proficiency
- (Desirable) Experience mentoring others and/or driving technical direction in a small, fast‐moving team
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.
What the job involves
- 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.
- 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.
Staff Machine Learning Performance Engineer (Inference Optimisation) in London employer: Wayve
At Wayve, we pride ourselves on being an innovative employer that fosters a collaborative and dynamic work culture, perfect for those passionate about self-driving technology. Our team is dedicated to employee growth, offering opportunities to work on high-impact projects while optimising machine learning inference for cutting-edge edge devices. Located in a vibrant tech hub, we provide a supportive environment where your contributions directly influence the future of autonomous driving.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Machine Learning Performance Engineer (Inference Optimisation) in London
✨Tip Number 1
Network like a pro! Attend industry meetups, conferences, or online webinars related to machine learning and performance engineering. It's a great way to meet potential employers and learn about job openings that might not be advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving performance optimisation in ML systems. This can really set you apart when you're chatting with hiring managers.
✨Tip Number 3
Prepare for technical interviews by brushing up on your debugging and profiling skills. Be ready to discuss how you've tackled performance issues in the past, as this will demonstrate your hands-on experience and problem-solving abilities.
✨Tip Number 4
Don't forget to apply through our website! We love seeing passionate candidates who are eager to make an impact. Tailor your application to highlight your experience with relevant stacks and tools, and let us know how you can contribute to our team.
We think you need these skills to ace Staff Machine Learning Performance Engineer (Inference Optimisation) in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience with performance optimisation in production systems. We want to see how your skills align with the specific requirements of the Staff Machine Learning Performance Engineer role.
Showcase Your Technical Skills:Don’t forget to mention your proficiency with relevant stacks like TensorRT or CUDA. We love seeing candidates who can quickly learn new frameworks, so share any experiences that demonstrate this ability!
Communicate Clearly:As a collaborative teammate, it’s important to show us your communication skills. Use clear language to explain your past projects and how you’ve aligned stakeholders on performance trade-offs. This will help us see how you fit into our team dynamic.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows your enthusiasm for joining our team!
How to prepare for a job interview at Wayve
✨Know Your Tech Stack
Make sure you’re well-versed in the relevant stack or toolchain mentioned in the job description, like TensorRT or CUDA. Brush up on your knowledge and be ready to discuss how you've used these tools to optimise performance in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific challenges you've faced in production systems, especially regarding latency, memory, or power constraints. Use concrete examples to illustrate how you identified bottlenecks and implemented effective solutions.
✨Communicate Clearly
As a collaborative teammate, it’s crucial to demonstrate your communication skills. Be prepared to explain complex technical concepts in simple terms and discuss how you’ve aligned stakeholders on performance trade-offs in previous roles.
✨Demonstrate Your Hands-On Experience
Since this role involves practical work across ML systems and embedded deployment, share your hands-on experiences. Discuss any projects where you optimised ML inference for edge devices and how you validated those improvements through benchmarking.
