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 culture.
- Benefits: Competitive salary, flexible working 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) employer: Wayve
At Wayve, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. As a Staff Machine Learning Performance Engineer, you'll have the opportunity to work on cutting-edge projects in the exciting field of self-driving technology, with ample opportunities for professional growth and mentorship. Our commitment to employee development, combined with our focus on impactful work in a fast-paced environment, makes Wayve a truly rewarding place to advance your career.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Machine Learning Performance Engineer (Inference Optimisation)
✨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 connect with potential employers and showcase your passion for the field.
✨Tip Number 2
Show off your skills! Create a portfolio of projects that highlight your experience with tools like TensorRT or CUDA. This not only demonstrates your technical abilities but also gives you something tangible to discuss during interviews.
✨Tip Number 3
Practice makes perfect! Prepare for technical interviews by solving coding challenges and optimising algorithms. Familiarise yourself with common performance bottlenecks and how to address them, so you can impress your interviewers.
✨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 relevant experience and how it aligns with our mission at Wayve.
We think you need these skills to ace Staff Machine Learning Performance Engineer (Inference Optimisation)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with performance optimisation in production systems. Use specific examples that showcase your skills with relevant tools like TensorRT or CUDA, and don’t forget to mention any work with embedded ML models!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for self-driving cars and how your background aligns with the role. Be clear about how you can contribute to optimising ML inference for edge devices and why you’re excited about joining our team.
Showcase Your Communication Skills:As a collaborative teammate, it’s important to demonstrate your ability to communicate effectively. Highlight experiences where you’ve aligned stakeholders on performance trade-offs or worked in a team to solve complex problems. We love clear communicators!
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 you’re keen on joining our awesome team at StudySmarter!
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 instances where you’ve identified and resolved performance bottlenecks. Use concrete examples that highlight your ability to operate at different levels of abstraction, from high-level model behaviour to low-level execution.
✨Communicate Clearly
As a collaborative teammate, it’s crucial to demonstrate your communication skills. Be ready to explain complex concepts in simple terms and discuss how you’ve aligned stakeholders on performance trade-offs in previous roles.
✨Demonstrate Your Passion
Since the role involves working on self-driving cars, express your enthusiasm for the field. Share any relevant projects or experiences that showcase your commitment to advancing technology in this area, and don’t hesitate to mention your eagerness to learn and adapt.
