Our interview process mirrors the work we do – rigorous, collaborative and built to uncover brilliance. Every stage is a chance to show how you think, solve problems and push boundaries.
Machine learning roles
At G-Research, machine learning is our edge – powering the models, systems and data that transform bold research into real-world results. If you’re ready to push the boundaries of what most think is possible, your place is here.
Machine learning vacancies
Quant Research
Internship in Quantitative Research
London
Machine Learning Research Internship
London
Natural Language Processing Internship
London
Data Vendor Analyst
London
Quantitative Researcher – Postgraduate
London
Machine Learning Researcher
London
Quantitative Researcher – Fundamental Equity Research
London
Quantitative Researcher – Experienced
London
Natural Language Processing Researcher
London
Quantitative Analyst
London
Why choose G-Research for your ML career?
Our machine learning team in London is the engine that drives our success. And we want you to be a part of it. You’ll work in a collaborative, research-driven environment, where engineers and researchers collaborate to tackle some of the most complex challenges in finance. With access to near limitless data and infrastructure, our researchers are given the freedom to experiment at the cutting-edge and move ideas from concept to production. After all, we’re not optimising existing models; we’re creating the ones others aspire to match.
We know performance is built on balance. That’s why we offer market-leading machine learning salaries, generous holidays and unmatched wellbeing perks designed to help you perform at your peak. And as you grow, your opportunities grow with you – through structured learning, internal mobility and the freedom to specialise or lead in a field that never stands still.
What the ML team say
"My intern experience was really good. You get the opportunity to impact a business, which is important if you’re preparing to enter the workplace. You get to do something useful and see how it gets used; I worked on a project that is still being used now."
"I get to build software that solves real problems for my colleagues, while also being part of the global Open Source community."
"My favourite part of working at G-Research is the people, from my colleagues to our customers. The culture is great and encourages collaboration, which makes it easier for everyone to work together."
"Before G-Research I’d completed internships at a few companies whilst studying Computer Science at the University of York. Since joining, I’ve progressed from an intern to a graduate to an engineer, having worked on systems and technology core to the business my entire time."
What is the application process?
Online Application
Our assessment process kicks off with our Talent Acquisition team, who will review your application and assess your fit for the role.
Stage One: Technical Interview
You will meet with a team member – or take a remote test – where your technical abilities will be put to the test.
Stage Two: Behavioural Interview
We will set aside technical skills and focus on you.
Stage Three: Further Technical Interviews
Here, we will take a deeper dive into your technical skills and competencies.
Stage Four: Management Interviews
The final stage of our interview process is where you will meet members of your team, your future manager, and functional leadership.
How to get a machine learning research job
There’s no single route into machine learning. We hire PhDs, postdocs and engineers pivoting from other technical fields. What matters most is capability – expertise in areas like deep learning, Bayesian methods, NLP or approximate inference, and the instinct to know when standard models won’t cut it. You’ll bring exceptional reasoning, mathematical depth and ideally, fluency in Python. Publications at NeurIPS, ICML or ICLR will help you stand out – but curiosity and skill matter most. If you’re ready to take the next step, our guide shows you how to build the expertise, confidence, and experience to break into the field.
