At a Glance
- Tasks: Optimise ML inference on edge devices and tackle performance bottlenecks.
- Company: Wayve, a cutting-edge tech company based in London.
- Benefits: Flexible hybrid working model and competitive salary.
- Other info: Exciting opportunity for growth in a dynamic tech environment.
- Why this job: Join a team pushing the boundaries of machine learning technology.
- Qualifications: Experience with production systems and tools like TensorRT and CUDA.
The predicted salary is between 70000 - 90000 € per year.
Wayve, located in London, is seeking a Staff ML Performance Engineer to optimize ML inference on edge devices. This full-time role includes responsibilities such as profiling bottlenecks in the inference stack and implementing optimisations in compilers and runtimes.
The ideal candidate has proven experience with production systems, strong proficiency in relevant tools like TensorRT and CUDA, and solid software engineering fundamentals.
The position offers a hybrid working model, allowing for flexibility between office and home.
Staff ML Inference Performance Engineer - Edge GPUs employer: Wayve
Wayve is an exceptional employer that fosters a dynamic and innovative work culture in the heart of London. With a strong emphasis on employee growth, we provide opportunities for professional development and collaboration on cutting-edge technology in machine learning. Our hybrid working model ensures flexibility, allowing you to balance your personal and professional life while contributing to meaningful projects that shape the future of edge computing.
StudySmarter Expert Advice🤫
We think this is how you could land Staff ML Inference Performance Engineer - Edge GPUs
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working with edge devices and ML inference. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! If you’ve got projects or contributions related to TensorRT or CUDA, make sure to highlight them in your conversations. Real-world examples speak volumes.
✨Tip Number 3
Prepare for technical interviews by brushing up on profiling techniques and optimisation strategies. We all know that being able to discuss bottlenecks confidently can set you apart from the crowd.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, we love seeing candidates who take that extra step.
We think you need these skills to ace Staff ML Inference Performance Engineer - Edge GPUs
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with ML inference and tools like TensorRT and CUDA. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about optimising ML performance on edge devices. We love seeing enthusiasm and a clear understanding of the role.
Showcase Your Problem-Solving Skills:In your application, mention specific challenges you've faced in production systems and how you tackled them. We appreciate candidates who can demonstrate their analytical thinking and technical prowess.
Apply Through Our Website:We encourage you to apply directly through our website for a smoother process. It helps us keep track of applications and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Wayve
✨Know Your Tech Inside Out
Make sure you brush up on your knowledge of TensorRT and CUDA. Be ready to discuss how you've used these tools in past projects, and think about specific examples where you've optimised ML inference. This will show that you not only understand the theory but also have practical experience.
✨Prepare for Technical Questions
Expect to face technical questions that dive deep into profiling bottlenecks and optimisation strategies. Practise explaining your thought process clearly and concisely. You might even want to run through some common scenarios or problems you’ve encountered and how you solved them.
✨Showcase Your Software Engineering Skills
Since solid software engineering fundamentals are key for this role, be prepared to discuss your coding practices, design patterns, and any relevant projects. Bring along examples of your work that demonstrate your ability to write clean, efficient code.
✨Embrace the Hybrid Model
With a hybrid working model in place, be ready to discuss how you manage your time and productivity between home and the office. Share any experiences you have with remote collaboration tools and how you ensure effective communication with your team, regardless of location.
