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Job description
We are seeking an exceptional ML Performance Engineer to optimise large-scale workloads across our GPU and CPU infrastructure. You will design and implement techniques that improve performance and capabilities of research workloads on cutting-edge compute infrastructure.
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.
Ideal candidate will have a 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.
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Company
Finance • Tech, Software & IT Services
G-Research is a quantitative finance research firm that leverages scientific rigor to tackle complex challenges in financial markets. The company unites world-class researchers and engineers, focusing on advanced quantitative research, machine learning, and robust IT infrastructure, systems, and information security. By combining deep exploration with methodical execution and a long-term perspective, G-Research delivers innovative, data-driven investment strategies and technology solutions. Its culture of disciplined analysis and collaborative problem-solving sets it apart in the industry, enabling clients and partners to achieve superior market insights and operational resilience.