G-Research

Quantitative finance research applying rigorous methods

Machine Learning Engineer

Platform EngineeringConfirmed live in the last 24 hours

Full-timeLondon, UKIn-person

About the Job

Requirements

Strong object-oriented engineering skills.
Experience building machine learning models at scale, a postgraduate degree in machine learning or a related field, or a demonstrable track record of success in online data-science competitions such as Kaggle.
The ability to work independently and in small teams on engineering challenges focused on machine learning and scientific computing.
The ability to apply advanced optimization methods, modern machine learning techniques, high-performance computing, profiling, or model-inference expertise.
The ability to engage with vendors, explore new technologies, and present results effectively to stakeholders.

Responsibilities

Evaluate alternative accelerators for machine learning workloads.
Develop and assess multi-node distributed training approaches to understand trade-offs in networking technology.
Optimize model inference to minimize latency or maximize throughput.
Understand and optimize storage technology to maximize bandwidth.
Evaluate the latest hardware and software in the machine learning ecosystem.
Liaise with vendors and provide constructive feedback on their products and roadmaps.
Collaborate with Quant Researchers, Quant ML Engineers, and other engineering groups on initiatives involving machine learning technologies.
Use quick one-off scripts for proofs of concept or design more complex systems when needed.

Desired Qualifications

Experience with Python, PyTorch, and NumPy.
Experience applying advanced optimization methods, modern machine learning techniques, high-performance computing, profiling, or model-inference techniques.
A postgraduate degree in machine learning or a related field, or commercial experience building machine learning models at scale; exceptional candidates with a demonstrated record in online data-science competitions such as Kaggle will also be considered.

We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity.

From our London HQ, we unite world-class researchers and engineers in an environment that values deep exploration and methodical execution - because the best ideas take time to evolve. Together we’re building a world-class platform to amplify our teams’ most powerful ideas.

As part of our engineering team, you’ll shape the platforms and tools that drive high-impact research - designing systems that scale, accelerate discovery and support innovation across the firm.

Take the next step in your career.

The role

We are looking for an exceptional Machine Learning Engineer to work in our ML and HPC Architecture team, identifying and working with tools at the cutting-edge of machine learning.

You will work closely with a wide range of internal G-Research teams, including Quant Researchers, Quant ML engineers and other engineering groups - as well as with external partners and experts.

You will collaborate across disciplines on a broad set of initiatives to help G-Research leverage the next generation of machine-learning technologies.

Past projects have included:

Evaluating alternative accelerators for ML workloads
Multi-node distributed training to understand trade-offs in networking technology
Optimising model inference to minimise latency or maximise throughput
Understanding and optimising different storage technology to maximise bandwidth
Evaluating the latest hardware and software in the machine learning ecosystem

Liaising with vendors and providing constructive feedback on their products and roadmaps

Who are we looking for?

You will be comfortable working both independently and in small teams on a variety of engineering challenges, with a particular focus on machine learning and scientific computing.

The ideal candidate will have the following skills and experience:

A postgraduate degree in ML or a related field, or bringing commercial experience building ML models at scale, we will also consider exceptional candidates with demonstrable track record of success in online data-science competitions, such as Kaggle
Strong object-oriented engineering skills, with experience in Python, PyTorch and NumPy desirable
The ability to apply advanced optimisation methods, modern ML techniques, HPC, profiling or model-inference expertise; you do not need to have all of the above
A passion for the latest ML and HPC trends, with genuine curiosity and enthusiasm
Excellent communication skills with the ability to work independently, engage with vendors, explore new technologies and present results effectively to stakeholder

Choose the right level of abstraction, using quick one-off scripts for proofs of concept or designing more complex systems when needed

Finance experience is not necessary for this role and candidates from non-financial backgrounds are encouraged to apply.

Why join us?

Highly competitive compensation plus annual discretionary bonus
Lunch provided (via Just Eat for Business) and dedicated barista bar
30 days’ annual leave
9% company pension contributions
Informal dress code and excellent work/life balance
Comprehensive healthcare and life assurance
Cycle-to-work scheme
Monthly company events

G-Research is committed to cultivating and preserving an inclusive work environment. We are an ideas-driven business and we place great value on diversity of experience and opinions.

We want to ensure that applicants receive a recruitment experience that enables them to perform at their best. If you have a disability or special need that requires accommodation please let us know in the relevant section

About the Company

G-Research

Quantitative finance research applying rigorous methods

G-Research applies scientific rigour to solve complex challenges in financial markets. It brings together world-class researchers and engineers to develop data-driven models and trading ideas, testing them through rigorous experimentation and robust infrastructure. Unlike many traditional asset managers, its culture emphasizes deep exploration, reproducibility, methodical execution, and long-term thinking. The goal is to generate alpha through disciplined, evidence-based research that informs investment decisions and advances understanding of financial markets.

Company size: 1,001-5,000
Company stage: Not listed
Total funding: Not listed
Headquarters: London, United Kingdom
Founded: 2001

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