Quant jobs / G-Research

Machine Learning Engineer

An ML and high-performance computing engineering role at a London quant firm. You assess and tune the hardware and software stack that quant researchers use to train and serve models.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Benchmark candidate accelerators and networking set-ups for training jobs
Tune multi-node distributed training runs
Cut latency or raise throughput in model inference
Test storage options for data-heavy ML workloads
Talk to vendors and feed back on their products

What they ask for

Required

Object-oriented engineering skills
Python
Familiarity with PyTorch and NumPy
Optimisation or HPC or inference expertise (not all needed)
Independent working and clear communication
Interest in ML and HPC trends

Preferred

Postgraduate ML degree or commercial ML-at-scale experience
Strong data-science competition record such as Kaggle

Education: Postgraduate ML degree, commercial experience, or strong competition record all considered
Experience: Postgraduate study or commercial experience building ML models at scale
Languages and tools: Python
PyTorch
NumPy

How to prepare

Train a small PyTorch model across several GPUs and measure scaling
Profile an inference pipeline and cut its latency
Learn how interconnects and storage limit training throughput
Practise explaining benchmark results to non-specialists

What to study

The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

Read the full posting on G-Research’s site before applying: requirements, location and pay can change after we check them.

View the posting on G-Research’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with G-Research and doesn’t handle applications.