Stanford Black Limited
Machine Learning Engineer
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Machine Learning Engineer
We're partnering with a highly quantitative research organisation building large-scale machine learning systems in a performance-critical environment. This role sits at the intersection of machine learning, distributed systems, and high-performance computing, with a focus on scaling modern ML workloads and improving the efficiency of training and inference for large models.
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.
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Requirements
- 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.
Highly Relevant Experience
- Distributed training technologies such as DeepSpeed, FSDP, Megatron, Ray, DDP or similar.
- GPU programming and optimisation (CUDA, Triton, NCCL, XLA, PTX).
- Multi-GPU or multi-node training environments.
- HPC, Slurm, Kubernetes, large-scale compute platforms, or cloud-based training infrastructure.
- Foundation models, LLMs, recommendation systems, ranking systems, or large-scale deep learning.
- Training efficiency, inference optimisation, compiler technologies, kernel optimisation, or systems-level ML performance work.
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Strongly Preferred
- Experience working with billion-parameter models or large-scale distributed training workloads.
- Contributions to ML infrastructure, training frameworks, open-source projects, or large-scale AI systems.
- Experience owning performance-critical systems in production environments.
- Publications or demonstrated technical expertise in machine learning systems, distributed computing, or optimisation.
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