Quant jobs / G-Research

Machine Learning Workflow Engineer

An MLOps-focused engineering role at a London quant firm, building the pipeline that takes ML research through to deployment. It supports quant research teams across the firm.

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.

Build feature stores and model stores for researchers
Version features, data and models consistently
Set up model serving to raise compute utilisation
Create CI/CD for ML and monitoring for models in production
Run model fits that depend on complex job graphs

What they ask for

Required

Sound software architecture
MLOps best practice
High-quality software engineering
Working with technical and non-technical colleagues
Independent prioritisation
Industry experience

Education: No degree level stated
Experience: Industry experience expected; years not stated

How to prepare

Build an end-to-end ML pipeline with versioned data, features and models
Learn a workflow orchestrator and model one dependency-heavy job graph
Add validation and drift monitoring to a deployed model
Study how feature stores avoid look-ahead leakage

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.