Machine Learning Engineer jobs in United Kingdom

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Oxford Knight

London, United Kingdom

Other

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bff6f7efc14f

33

12.08.2025

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Summary:

Exciting opportunity to work at a tech-centric prop trading fund which trades a wide range of financial products, with offices across the globe. Looking for an experienced engineer with low-level systems programming and optimization expertise to join their growing ML team.

Machine learning is front and centre at this firm, and your focus will be to optimize the performance of their models: both training and inference. They’re interested in efficient large-scale training, low-latency inference in real-time systems, and high-throughput inference in research. Partly this will involve improving straightforward CUDA, but they also need a whole-systems approach, including storage systems, networking, and host- and GPU-level considerations.

The successful candidate will be a smart, curious software engineer who enjoys finding solutions for complex problems. If you also have a great appetite for learning new things, this role is for you!

Requirements:

• An understanding of modern ML techniques and toolsets, with a strong focus on performance
• The systems knowledge & experience required to debug a training run’s performance end to end
• Low-level GPU and compute cluster knowledge, CUDA or other types of GPU programming, e.g. PTX, SASS, warps, cooperative groups, Tensor Cores, & the memory hierarchy
• Debugging/optimization tooling experience, e.g. CUDA GDB, NSight Systems, NSight Compute, etc.
• Library knowledge of Triton, CUTLASS, CUB, Thrust, cuDNN, and cuBLAS
• Generous benefits package, including physical & mental health benefits, excellent holiday entitlement, significant parental leave, retirement benefits, private on-site gym
• Focus on learning & development with tuition reimbursement
• Recreation spaces with breakfast, lunch, snacks and treats

Whilst we carefully review all applications, to all jobs, due to the high volume of applications we receive it is not possible to respond to those who have not been successful.