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- Tasks: Optimise large-scale ML workloads across GPU and CPU infrastructures.
- Company: Leading financial technology firm based in London.
- Benefits: Competitive salary, comprehensive healthcare package, and great work-life balance.
- Why this job: Join a dynamic team and enhance performance for cutting-edge research projects.
- Qualifications: Degree in computer science and experience with distributed workloads required.
- Other info: Exciting opportunities for career growth in a fast-paced environment.
The predicted salary is between 42000 - 84000 £ per year.
A financial technology firm based in London seeks an exceptional ML Performance Engineer to optimize workloads across GPU and CPU infrastructures. The role involves designing techniques that enhance performance for research workloads, directly collaborating with research teams.
Ideal candidates will have degrees in computer science and experience with distributed workloads, proficiency in Python, C++, CUDA, and knowledge of deep learning frameworks.
Attractive benefits include competitive compensation and a comprehensive healthcare package.
ML Performance Engineer: Optimize Large-Scale ML Compute in London employer: G-Research
Contact Detail:
G-Research Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Performance Engineer: Optimize Large-Scale ML Compute in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working in financial tech or ML. A friendly chat can open doors and give you insights that might just land you an interview.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python, C++, or CUDA. This is your chance to demonstrate your expertise in optimising workloads and deep learning frameworks.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of distributed workloads. Practice coding challenges and be ready to discuss your past experiences with research teams and performance optimisation.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace ML Performance Engineer: Optimize Large-Scale ML Compute in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with distributed workloads and the programming languages mentioned in the job description. We want to see how your skills align with what we're looking for!
Showcase Your Projects: If you've worked on any relevant projects, especially those involving GPU and CPU infrastructures, be sure to include them. We love seeing practical examples of your work that demonstrate your expertise.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about optimising ML performance. Share your thoughts on how you can contribute to our research teams and make an impact at StudySmarter.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at G-Research
✨Know Your Tech Inside Out
Make sure you brush up on your knowledge of Python, C++, and CUDA. Be ready to discuss how you've used these languages in past projects, especially in optimising workloads. It’s a good idea to have specific examples that showcase your skills in distributed workloads.
✨Understand the Company’s Focus
Research the financial technology firm thoroughly. Understand their products, services, and the specific challenges they face in optimising ML performance. This will help you tailor your answers and show that you're genuinely interested in contributing to their goals.
✨Prepare for Technical Questions
Expect technical questions related to deep learning frameworks and performance optimisation techniques. Practise explaining complex concepts in simple terms, as you may need to collaborate with research teams who might not have a technical background.
✨Showcase Your Collaboration Skills
Since the role involves working closely with research teams, be prepared to discuss your experience in collaborative environments. Share examples of how you’ve successfully worked with others to solve problems or enhance performance in previous roles.
