Machine Learning Performance Engineer - Quant Research & Trading

Job Description

We’re looking for ML Performance Engineers to join a scientific led systematic trading firm to design, optimize, and deploy large-scale machine learning systems that directly impact trading performance. You’ll optimize large-scale deep learning and LLM pipelines, turning cutting-edge research into measurable P&L impact.

Day to Day:

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• Build and optimize large-scale ML training & inference pipelines
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• Enhance deep learning frameworks (PyTorch, JAX, TensorFlow) for performance
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• Debug GPU, memory, and distributed training bottlenecks
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• Collaborate with researchers to deploy models in live trading systems
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What We’re Looking For:

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• Strong ML fundamentals (transformers, LLMs, attention, RLHF)
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• Deep GPU expertise (CUDA, Tensor Cores, warp-level ops)
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• Proficiency in Python & C++
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• Knowledge of deep-learning frameworks like PyTorch, JAX
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• GPU Libraries and tools – Triton, CUB, CuDNN, cuBLAS
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Why Join:

Work with world-class researchers solving some of finance’s hardest problems with extensive room to push boundaries. Expect technical depth, real-world impact, and a culture that prizes curiosity, rigor, and speed.

Apply or get in touch for more info!