Machine Learning Specialist

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

• We're partnering with a highly quantitative research organisation building some of the most advanced machine learning systems in industry.
• Engineers in this team operate at the intersection of machine learning, distributed systems, and high-performance computing, helping scale modern AI workloads across a large GPU estate. The work spans distributed training, inference optimisation, compute infrastructure, systems design, and performance engineering.
• You'll work directly with researchers to take cutting-edge ML ideas from prototype to production, solving problems that span software, hardware, networking, compilers, and large-scale distributed systems.
• This is an opportunity to tackle technical challenges rarely seen outside leading AI labs and top-tier quantitative research firms.

Responsibilities

• Design and optimise large-scale training and inference systems for modern ML workloads.
• Improve throughput, latency, GPU utilisation and training efficiency across distributed environments.
• Build infrastructure and tooling that accelerates 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 critical ML infrastructure used across the organisation.

What We're Looking For

• Strong experience in Machine Learning Engineering, Research Engineering, ML Infrastructure, Distributed Systems or Performance Engineering.
• Excellent software engineering skills in Python and/or C++.
• Experience working with modern ML frameworks such as PyTorch, JAX or TensorFlow.
• Experience training, deploying or optimising large-scale machine learning models.
• Strong understanding of distributed systems, parallel computing and performance optimisation.
• Degree in Computer Science, Mathematics, Physics, Engineering or a related quantitative discipline, or equivalent industry experience.

Particularly Relevant Experience

• Large-scale distributed training (DeepSpeed, FSDP, Megatron, Ray, DDP or similar).
• GPU programming and optimisation (CUDA, Triton, NCCL, XLA, PTX).
• Multi-GPU or multi-node training environments.
• HPC, Kubernetes, Slurm or large-scale compute infrastructure.
• Foundation models, LLMs, recommendation systems or large-scale deep learning.
• Compiler technologies, kernel optimisation, inference optimisation or systems-level ML performance work.

Why Join?

• Work on some of the largest and most computationally intensive ML workloads in industry.
• Solve challenging problems across distributed systems, GPU computing, machine learning infrastructure and performance optimisation.
• Collaborate closely with exceptional researchers, engineers and quantitative scientists.
• Significant autonomy and ownership from day one.
• Deep investment in compute infrastructure and engineering excellence.
• Competitive compensation and bonus structure.