At a Glance
- Tasks: Optimise ML inference for edge devices and contribute to high-impact projects.
- Company: Wayve, a leader in Embodied AI technology for automated driving.
- Benefits: Competitive salary, inclusive culture, and opportunities for career growth.
- Other info: Dynamic team environment with a focus on collaboration and innovation.
- Why this job: Make a real impact on the future of autonomous driving with cutting-edge technology.
- Qualifications: Experience in performance optimisation and strong software engineering skills.
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.
Staff ML Performance Engineer (Inference Optimisation) in London employer: Gravity Engineering Services Pvt Ltd.
At Wayve, we pride ourselves on being a pioneering employer in the field of Embodied AI technology, where your contributions directly impact the future of automated driving. Our inclusive work culture fosters collaboration and innovation, providing ample opportunities for professional growth and development in a fast-paced environment. Join us in London, where you will work alongside talented individuals on high-impact projects that challenge the status quo and drive meaningful change in the industry.
Contact Details:
Gravity Engineering Services Pvt Ltd. 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! Attend industry meetups, conferences, or online webinars related to AI and ML. Engaging with professionals in the field can open doors and give you insights into unadvertised job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML performance engineering. This gives potential employers a tangible sense of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios relevant to the role. We recommend doing mock interviews with friends or using platforms that simulate real interview conditions.
✨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 your genuine interest in joining our team at Wayve.
We think you need these skills to ace Staff ML Performance Engineer (Inference Optimisation) in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Staff ML Performance Engineer role. Highlight your experience with performance optimisation and relevant tools like TensorRT or CUDA. We want to see how your skills align with our mission at Wayve!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for AI and how you can contribute to our vision of creating smarter, safer driving solutions. Be sure to mention any specific projects that showcase your expertise in ML inference.
Showcase Your Problem-Solving Skills:In your application, don’t just list your skills—show us how you've tackled complex challenges in the past. We love candidates who embrace uncertainty and can demonstrate their ability to deliver impactful solutions under tight constraints.
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 team at Wayve!
How to prepare for a job interview at Gravity Engineering Services Pvt Ltd.
✨Know Your Tech Stack
Make sure you’re well-versed in the relevant stacks and toolchains mentioned in the job description, like TensorRT and 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 solved performance bottlenecks. Be ready to explain your thought process and the impact of your solutions, especially in production systems with tight constraints like latency and memory.
✨Communicate Clearly
Since collaboration is key in this role, practice articulating your ideas clearly. Think about how you can align stakeholders on performance trade-offs and priorities, and be prepared to demonstrate your ability to work as part of a team.
✨Get Hands-On with Benchmarking
Familiarise yourself with benchmarking techniques and regression testing. If you have experience with real devices, be ready to share insights on how you handled system-level constraints and ensured performance improvements across models and software releases.
