Machine learning engineer in quantitative finance

A leading quantitative research firm applies scientific clarity to the most complex problems in finance. They build next-generation machine learning and high-performance computing platforms, turning novel research into tradable signals. This role sits at the intersection of advanced ML and HPC architecture, shaping the platforms and tools that empower quantitative researchers. You won't be building models in isolation; instead, you'll evaluate, integrate, and optimize the underlying technologies that amplify research velocity, working closely with both internal experts and external vendors.

• Evaluate and integrate cutting-edge ML accelerators and HPC technologies.
• Optimize model inference for extreme latency and throughput requirements.
• Benchmark distributed training setups, understanding networking and storage trade-offs.
• Engage directly with leading technology vendors, influencing their product roadmaps.
• Design and implement solutions, from rapid proofs-of-concept to robust system architectures.

• A postgraduate degree in Machine Learning or a related quantitative field, or equivalent commercial experience building ML systems at scale.
• Strong object-oriented programming skills, particularly with Python, PyTorch, and NumPy.
• Deep expertise in at least one of: advanced optimization methods, modern ML techniques, high-performance computing, profiling, or model inference.
• A demonstrated ability to evaluate and integrate novel ML/HPC technologies, engaging with external experts and vendors.

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