Machine Learning Systems / AI Infrastructure Engineer- Quant / Systematic Trading Firms
Core
Build and optimise the infrastructure behind rapidly expanding machine learning capabilities for quantitative trading firms, focusing on large-scale distributed training, GPU infrastructure, and high-performance inference.
Role type
Senior IC machine learning systems engineer (GPU/infrastructure)
Builds
Large-scale distributed training clusters, GPU compute estates, low-latency inference pipelines, and research platforms for model iteration.
Domain
Quantitative finance + High-Performance Computing (HPC)
Deliverable
production ML models
Required skills
Distributed systems, GPU/CUDA engineering, HPC, ML frameworks, Python, C++, networking (InfiniBand, RoCE, NVLink), profiling, resource scheduling
Preferred skills
Triton, low-level systems optimisation, research engineering, MLOps
Technologies
PyTorch, CUDA, Triton, InfiniBand, RoCE, GPUDirect, NVLink
Responsibilities
Optimise GPU kernels and memory management, design high-throughput inference systems, build distributed training pipelines, develop research experimentation platforms, profile end-to-end training workloads, automate model deployment.
Seniority
Senior, hands-on IC