ML Infrastructure Engineer - Distributed Systems & HPC

Stanford Black Limited; Greater London; On-site; GBP 100,000 - 150,000; Full time

Benefits offered by this job

Competitive compensation

Bonus structure

Autonomy from day one

Close collaboration with researchers

Job summary

Stanford Black Limited, London, is seeking an ML infrastructure engineer to design and optimize large-scale training and inference systems. You will work with researchers to scale ML workloads across a GPU estate, spanning software, hardware, networking, and compilers.

The role offers significant autonomy, collaboration with researchers, and a focus on performance across distributed systems. Strong Python/C++ engineering and experience with PyTorch/TensorFlow are expected, with opportunities to

Qualifications

• Degree in CS/Math/Physics/Engineering or related field.
• Strong software engineering in Python and C++.
• Experience with PyTorch, JAX or TensorFlow.

Responsibilities

• Design and optimise large-scale training and inference systems for ML workloads.
• Improve throughput, latency, GPU utilisation and efficiency across distributed environments.
• Build infrastructure and tooling to accelerate experimentation and model development.
• Partner with researchers to productionise novel ML approaches.
• Drive performance improvements across software, hardware and networking layers.
• Influence the technical direction of ML infrastructure.

Skills

Python

C++

PyTorch/TensorFlow

Distributed Systems

Performance Optimisation

Research Engineering

Education

Degree in CS/Math/Physics/Engineering

Tools

CUDA

GPGPU Tools

Kubernetes

Job description

Stanford Black Limited, London, is seeking an ML infrastructure engineer to design and optimize large-scale training and inference systems. You will work with researchers to scale ML workloads across a GPU estate, spanning software, hardware, networking, and compilers.

The role offers significant autonomy, collaboration with researchers, and a focus on performance across distributed systems. Strong Python/C++ engineering and experience with PyTorch/TensorFlow are expected, with opportunities to