Stanford Black Limited
Machine Learning Specialist
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Machine Learning Engineer
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.
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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.
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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.
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