At a Glance
- Tasks: Optimise large-scale workloads and enhance performance on cutting-edge compute infrastructure.
- Company: Join a leading firm in quantitative finance with a focus on innovation and collaboration.
- Benefits: Competitive pay, generous leave, healthcare, and a fun work environment with monthly events.
- Other info: Enjoy a flexible dress code and a strong commitment to inclusivity.
- Why this job: Make a real impact by shaping the future of machine learning in finance.
- Qualifications: Degree in computer science or equivalent, with skills in Python, C++, and deep learning frameworks.
The predicted salary is between 60000 - 80000 £ per year.
We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity. From our London HQ, we unite world‑class researchers and engineers in an environment that values deep exploration and methodical execution - because the best ideas take time to evolve. Together we’re building a world‑class platform to amplify our teams’ most powerful ideas.
As part of our engineering team, you’ll shape the platforms and tools that drive high‑impact research - designing systems that scale, accelerate discovery and support innovation across the firm.
The role
We are seeking an exceptional ML Performance Engineer to optimise large‑scale workloads across our GPU and CPU infrastructure. This is a hands‑on, impactful role. You will design and implement techniques that improve performance and capabilities of research workloads on cutting‑edge compute infrastructure, ensuring our researchers and engineers can make the best use of current and future systems. You will work directly with internal research teams and infrastructure engineers to profile and analyse workloads, eliminate bottlenecks and develop reference solutions. Your work will influence long‑term platform evolution and help shape the architecture, software stack and tooling that underpins large‑scale machine learning computation.
Key responsibilities
- Collaborating with researchers, senior stakeholders and engineers to understand their compute challenges and design optimised solutions.
- Profiling, benchmarking and tuning large‑scale training and inference workloads for performance on distributed CPU, GPU and memory‑intensive jobs.
- Developing reference implementations, libraries and tools to improve job efficiency and reliability.
- Collaborating closely with systems, architecture and platform teams to evolve our compute stack.
- Influencing long‑term platform and infrastructure decisions.
Qualifications
- Bachelors, Masters or PhD degree in computer science, or equivalent experience.
- Proven track record of profiling, benchmarking and optimising distributed workloads.
- Strong knowledge of Python, C++ and CUDA.
- Strong understanding of one or more deep learning frameworks, such as PyTorch.
- Strong background in data structures, algorithms and parallel programming on heterogeneous systems.
- Deep understanding of Linux OS fundamentals, such as scheduling, memory management, NUMA, networking and filesystems.
- Experience with HPC schedulers and Kubernetes‑based workload orchestration.
- Familiarity with profiling and monitoring tools, such as nsys, ncu, eBPF‑based tools and performance counters.
- Strong communication skills with the ability to collaborate across research, infrastructure and engineering teams.
Benefits
- Highly competitive compensation plus annual discretionary bonus.
- Lunch provided (via Just Eat for Business) and dedicated barista bar.
- 35 days’ annual leave.
- 9% company pension contributions.
- Informal dress code and excellent work/life balance.
- Comprehensive healthcare and life assurance.
- Cycle‑to‑work scheme.
- Monthly company events.
G-Research is committed to cultivating and preserving an inclusive work environment. If you have a disability or special need that requires accommodation, please let us know in the relevant section.
Machine Learning Performance Engineer employer: Barlowe LLP
At G-Research, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration in the heart of London. Our commitment to employee growth is evident through our competitive compensation packages, extensive benefits including 35 days of annual leave, and a supportive work environment that encourages exploration and creativity. Join us to be part of a world-class team dedicated to tackling complex challenges in quantitative finance while enjoying a healthy work/life balance and opportunities for professional development.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Performance Engineer
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, conferences, or even online webinars. We can’t stress enough how important it is to build relationships; you never know who might help you land that dream job.
✨Show Off Your Skills
Don’t just tell them what you can do—show them! Create a portfolio of your projects, especially those related to machine learning and performance engineering. We love seeing real-world applications of your skills, so make sure to highlight your best work.
✨Ace the Interview
Prepare for technical interviews by brushing up on your knowledge of Python, C++, and CUDA. We recommend practicing coding challenges and discussing your past experiences with profiling and optimising workloads. Confidence is key, so get ready to impress!
✨Apply Through Our Website
We encourage you to apply directly through our website. It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team and tackling those complex problems together!
We think you need these skills to ace Machine Learning Performance Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Performance Engineer role. Highlight your experience with profiling, benchmarking, and optimising workloads, as well as your knowledge of Python, C++, and CUDA. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about optimising large-scale workloads and how you can contribute to our team. Be sure to mention any relevant projects or experiences that showcase your expertise.
Showcase Your Collaboration Skills:Since this role involves working closely with researchers and engineers, make sure to highlight your collaboration skills in your application. Share examples of how you've successfully worked in teams to solve complex problems in the past.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people. Don’t miss out on this opportunity!
How to prepare for a job interview at Barlowe LLP
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, C++, and CUDA. Brush up on your knowledge of deep learning frameworks such as PyTorch, and be ready to discuss how you've used them in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've optimised workloads or eliminated bottlenecks. Think about the challenges you faced and how you approached them, as this will demonstrate your hands-on experience and analytical thinking.
✨Collaborate Like a Pro
Since the role involves working closely with researchers and engineers, be ready to talk about your collaboration experiences. Highlight instances where you’ve successfully communicated complex ideas or worked in a team to solve technical problems.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s current compute challenges or future projects. This shows your genuine interest in the role and helps you understand how you can contribute to their goals.
