Senior ML Systems Engineer: Large-Scale Performance

Stanford Black Limited; England; On-site; GBP 70,000 - 150,000; Full time

Job summary

Stanford Black Limited partners with a highly quantitative research organisation to build large-scale machine learning systems in a performance-critical environment. The role sits at the intersection of ML, distributed systems, and high-performance computing, focusing on scaling modern ML workloads and improving training and inference efficiency for large models.

Responsibilities include designing and optimising training and inference systems, improving throughput, latency, memory efficiency,

Qualifications

• 6+ years’ experience in Machine Learning Engineering, Research Engineering, ML Infrastructure, Distributed Systems, or Performance Engineering.
• Strong Python and/or C++ development experience.
• Deep understanding of modern ML frameworks including PyTorch, JAX, or TensorFlow.
• Experience training, deploying, or optimising large-scale machine learning models.
• Strong understanding of parallel computing, distributed systems, and performance optimisation.
• Degree (or equivalent experience) in Computer Science, Mathematics, Physics, Engineering, or a related quantitative discipline.

Responsibilities

• Design and optimise large-scale training and inference systems.
• Improve throughput, latency, memory efficiency, and GPU utilisation across distributed workloads.
• Partner with researchers to translate new ML ideas into scalable production systems.
• Build infrastructure and tooling that accelerates experimentation, model development, and deployment.
• Drive technical direction across performance-critical ML systems and compute infrastructure.
• Solve challenging problems spanning software, hardware, compilers, and distributed computing.

Skills

Python

C++

Machine Learning

Distributed Systems

Performance Engineering

Education

Degree in CS/Math/Physics/Engineering

Tools

PyTorch

JAX

TensorFlow

DeepSpeed/Megatron

Job description

Stanford Black Limited partners with a highly quantitative research organisation to build large-scale machine learning systems in a performance-critical environment. The role sits at the intersection of ML, distributed systems, and high-performance computing, focusing on scaling modern ML workloads and improving training and inference efficiency for large models.

Responsibilities include designing and optimising training and inference systems, improving throughput, latency, memory efficiency,