Senior Machine Learning Systems Engineer; Frameworks & Tooling Job London England UK,Software Development

Position: Senior Machine Learning Systems Engineer (Frameworks & Tooling)
Location: Greater London

We're looking for a senior engineer to help build, maintain and evolve the training framework that powers our frontier-scale language models. This role sits at the intersection of large-scale training, distributed systems, and HPC infrastructure
You will design and maintain the core components that enable fast, reliable, and scalable model training - and build the tooling that connects research ideas to thousands of GPUs
If you enjoy working across the full stack of ML systems, this role gives you the opportunity and autonomy to have massive impact
Build and own the training framework responsible for large-scale LLM training
Design distributed training abstractions (data/tensor/pipeline parallelism, FSDP/ZeRO strategies, memory management, checkpointing)
Improve training throughput and stability on multi-node clusters (e.g., GB200/300, AMD, H200/100)
Develop and maintain tooling for monitoring, logging, debugging, and developer ergonomics
Collaborate closely with infra teams to ensure our cluster, container environments, and hardware configurations support high-performance training
Investigate and resolve performance bottlenecks across the ML systems stack
Build robust systems that ensure reproducible, debuggable, large-scale runs
You'll work on some of the most challenging and consequential ML systems problems today
You'll collaborate with a world-class team working fast and at scale
You'll have end-to-end ownership over critical components of the training stack
You'll shape the next generation of infrastructure for frontier-scale models
You'll build tools and systems that directly accelerate research and model quality
Sample Projects:
Build a high-performance data loading and caching pipeline
Implement performance profiling across the ML systems stack
Develop internal metrics and monitoring for training runs
Build reproducibility and regression testing infrastructure
Develop a performant fault-tolerant distributed checkpointing system

Benefits

Six weeks' paid vacation
Equity / stock options
RRSP, 401(k), and Pension Scheme contributions
Coverage for 100% of your insurance premiums across health, dental, vision, and travel
Additional coverage for accessing mental health providers/services
Six months of fully paid parental leave, including adoption and surrogacy
Financial support for egg freezing and IVF in Canada and the UK
A monthly fitness and wellness allowance
Globally dispersed company that supports a remote work culture
A $2,000 annual education benefit for professional development
A weekly stipend for meals when working remotely and catered lunch when working from one of our global offices
A monthly arts and culture allowance
A monthly quality time allowance

A track record of building tools that increase developer velocity for ML teams

Experience with multi-node cluster orchestration (Slurm, Ray, Kubernetes, or similar)
Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JAX, AAAI, Nature, COLING, ACL, EMNLP)

Experience with training LLMs or other large transformer architectures

Deep familiarity with JAX internals, distributed training libraries, or custom kernels/fused ops Strong engineering experience in large-scale distributed training or HPC systems

Comfort debugging performance issues across CUDA/NCCL, networking, IO, and data pipelines

Excellent judgment around trade-offs: performance vs complexity, research velocity vs maintainability

Experience with data pipeline optimization, sharded datasets, or caching strategies

Contributions to ML frameworks (PyTorch, JAX, Deep Speed, Megatron, xFormers, etc.)Experience working with containerized environments (Docker, Singularity/Apptainer)
Background in performance engineering, profiling, or low-level systems

If some of the above doesn't line up perfectly with your experience, we still encourage you to apply!

Strong collaboration skills - you'll work closely with infra, research, and deployment teams

Familiarity with evaluation and serving frameworks (vLLM, TensorRT-LLM, custom KV caches)

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