Senior Machine Learning Engineer (Optimization)

A leading quantitative trading firm is expanding its machine learning capabilities and looking for an experienced Machine Learning Engineer to help build the infrastructure behind its next generation of deep learning systems.

This is a highly technical, hands-on position sitting at the intersection of ML systems, distributed computing, GPU optimization, and applied deep learning. You’ll work directly with quantitative researchers and engineers to build scalable systems for training, evaluating, deploying, and serving models used in live financial markets.

What You’ll Work On

• Architect scalable infrastructure for distributed model training and high-performance inference
• Build and optimize multi-GPU and multi-node ML workloads
• Improve GPU utilization, training throughput, inference latency, and overall system performance
• Develop production ML tooling spanning experimentation, model versioning, deployment, monitoring, and reproducibility
• Partner directly with researchers to move deep learning models from experimentation into production
• Support modern architectures including Transformers, sequence models, GNNs, and other deep learning systems
• Design reliable ML systems capable of operating over extremely large datasets and compute environments
• Help establish technical standards and influence the long-term direction of the firm's ML platform

What We’re Looking For

• 8 years of experience building ML platforms at a leading quantitative firm, research lab, or big tech
• Significant experience building production ML infrastructure or ML platforms from scratch
• Strong Python plus C and/or CUDA
• Deep experience with PyTorch, JAX, or TensorFlow
• Hands-on experience with distributed training and GPU-accelerated workloads
• Experience optimizing model training and/or inference performance at scale
• Strong understanding of modern deep learning architectures, particularly Transformers
• Experience deploying and operating ML models in demanding production environments
• Proficiency in deep learning fundamentals such as optimization, regularization, and loss-design.

Particularly Relevant Experience

Experience with technologies such as NCCL, TensorRT, CUDA, Kubernetes, distributed GPU clusters, custom kernels, model serving, or large-scale training frameworks would be highly relevant.

This is an opportunity to have significant ownership over a growing ML platform rather than simply maintaining established infrastructure. The team is investing heavily in compute and machine learning, with engineers working directly alongside researchers on systems that have measurable impact in live trading.