Machine Learning Engineer - Inference

You will work as part of an established and growing research team, and support in the productionisation of large/complex models for maximum trading impact. You'll closely collaborate with researchers, hardware and software engineers, and leverage your knowledge of GPU hardware and libraries to drive the optimisation of inference & hardware utilisation.

Your Core Responsibilities: Leverage your knowledge of GPU architecture & libraries to optimise model structure, library use and software-hardware integration Deliver extensions, fixes and improvements on top of base CUDNN & TensorRT to support performant evaluation Build C++ and CUDA based performance enhancement libraries for common deep learning libraries like Pytorch and JAX Your Skills and Experience: MS degree in CS or similar fields or equivalent experience 3+ years of relevant work experience Experienced in fundamental libraries for accelerating ML workflows, like CUDNN/TensorRT, ROCm, OpenVino or OpenPPL (understanding of one or more ML communication frameworks like NCCL is an advantage) Background in deep learning fundamentals and common deep learning software, especially PyTorch Experienced in C++ & CUDA. Experience in Python is also highly desirable About Us: IMC is a leading global market maker with offices in Europe, the US, and Asia-Pacific. We use algorithmic trading and advanced technology to buy and sell securities across multiple trading venues. Our vital role at the center of the financial markets provides liquidity for buyers and sellers worldwide.

In the late 1980s, IMC quickly identified the importance of technology in the fast-evolving trading environment. Our passion for innovation continues to drive us today. Although our company has grown rapidly over recent decades, we still approach our work with the same entrepreneurial spirit, emphasizing continuous improvement and innovation. We like our people to think for themselves, and we give them the responsibility and support they need to make an impact.

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