London | £250k-£700k+ TC | Quant Trading / Systematic FundsI'm working with several of the world's leading quantitative trading firms and systematic investment managers looking for exceptional engineers to build and optimise the infrastructure behind their rapidly expanding machine learning capabilities.
These aren't conventional ML application roles. The focus is on the
systems that make cutting-edge ML research possible: large-scale distributed training, GPU infrastructure, high-performance inference, research platforms and the tooling that allows researchers to iterate quickly and deploy models into production.
The scale can be exceptional. Across the top end of the market, firms are operating with petabytes or even exabytes of data, enormous CPU compute clusters and GPU estates numbering in the tens of thousands.
Depending on the team, you could work on:- Distributed model training at significant scale
- GPU compute and cluster infrastructure
- Low-latency and high-throughput inference
- Training and inference pipelines
- Research platforms and experimentation infrastructure
- ML frameworks and developer tooling
- GPU kernel and CUDA optimisation
- Distributed data loading and storage
- Scheduling and resource utilisation
- Networking for GPU clusters - InfiniBand, RoCE, GPUDirect, NVLink
- Profiling training workloads end-to-end
- Improving model iteration speed and researcher productivity
- Taking new ML techniques from research into production
The most performance-focused teams approach ML as a
whole-systems engineering problem. Optimising a model can mean looking beyond PyTorch into CUDA kernels, GPU memory, host performance, storage, networking, distributed collectives and the underlying hardware.
Other teams sit slightly higher in the stack, building production ML platforms and tooling, designing APIs for researchers, automating training/validation/monitoring and turning open-ended modelling requirements into robust engineering systems.
I'm interested in speaking with engineers from backgrounds including:- ML Infrastructure / ML Systems
- AI Infrastructure
- Distributed Training
- ML Performance
- GPU / CUDA Engineering
- HPC
- Distributed Systems
- Research Engineering
- MLOps / ML Platforms
- Large-scale inference
Strong Python is relevant across much of the market, while C++, CUDA, Triton and lower-level systems expertise become increasingly important for performance-focused teams.
Prior finance experience is not required. These firms are actively relevant to engineers coming from Big Tech, frontier AI labs, GPU/semiconductor companies, cloud infrastructure, HPC and other technically demanding environments. Jane Street, for example, explicitly notes that many of its ML performance engineers had not previously considered finance.
The attraction is the combination of
frontier-scale ML infrastructure, unusually direct access to researchers, enormous compute resources, short feedback loops and some of the strongest compensation available in engineering.I represent a concentrated group of leading quantitative trading firms, systematic funds and electronic market makers. This advert represents multiple active mandates rather than a vacancy with one specific firm.
Whilst we carefully review all applications, to all jobs, due to the high volume of applications we receive it is not possible to respond to those who have not been successful.
ContactIf you think you're a good match, or would like further information, please contact:
Henry Abbot+44 (0)
in/henry-abbot
:quality(80))
:quality(80)){kind=icon}
:quality(80))