Quantitative research & machine learning

G-Research
City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Quantitative Researcher

Machine Learning Engineer

Machine Learning Workflow Engineer

Machine Learning Researcher

Machine Learning Engineer

Machine Learning Workflow Engineer

Overview

Quantitative research & machine learning at our Research Lab tackles complex challenges in quantitative finance using deep mathematical, statistical and scientific rigour. We combine cutting-edge technology with world-class resources to create algorithmic platforms for our clients. Researchers analyse vast, complex datasets to uncover deep, actionable insights, test hypotheses, build models and receive instant feedback to accelerate innovation. Advanced optimisation techniques are designed to extract maximum value from ideas.

Machine learning

Our researchers challenge the efficient market hypothesis every day, applying cutting-edge machine-learning techniques at scale. They harness massive compute power and adapt methods from the latest research or in-house development to maintain a competitive edge.

Machine Learning College

We develop talent through G-Research Machine Learning College. Our researchers arrive from leading global institutions, often after PhDs or postdoctoral work, with publications at major conferences. They work autonomously within a collaborative environment that values curiosity, creativity and deep thinking.

What our people say

Our open culture and freedom for researchers to pursue valuable directions are highlighted by many who interview and work here, with emphasis on smart colleagues, work-life balance and collaboration across teams.

Our people, culture and environment

We foster a collaborative and dynamic environment where researchers, engineers and quantitative scientists work together to learn and grow. The culture supports curiosity, creativity and intellectual challenge.

Interview process
  1. Stage two: Technical interviews — typically four interviews focusing on mathematics; two if ML-focused, each lasting one hour. Expect questions on mathematics, programming and statistics relevant to the space.
  2. Stage three: Leadership interviews — meetings with company leaders after technical interviews.
  3. Online application — CV/resume and basic details, with updates on status within one week.
  4. Interview preparation guide — you may complete a quantitative aptitude assessment or an ML-specific test, depending on background.
How to apply

Looking to make an impact at one of the world’s leading quantitative research and technology firms? See our open roles and apply now.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.