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:
- Build and optimize large-scale ML training & inference pipelines
- Enhance deep learning frameworks (PyTorch, JAX, TensorFlow) for performance
- Debug GPU, memory, and distributed training bottlenecks
- Collaborate with researchers to deploy models in live trading systems
What We’re Looking For:
- Strong ML fundamentals (transformers, LLMs, attention, RLHF)
- Deep GPU expertise (CUDA, Tensor Cores, warp-level ops)
- Proficiency in Python & C++
- Knowledge of deep-learning frameworks like PyTorch, JAX
- GPU Libraries and tools – Triton, CUB, CuDNN, cuBLAS
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!
Machine Learning Performance Engineer - Quant Research & Trading in New York City Metropolitan Area at Acquire Me
This position is listed as full time and onsite. It was posted yesterday.