“I’m a Machine Learning Engineer, primarily supporting Researchers trying to find signals in the market. Typically, I do that by building complicated machine learning models – and a lot of engineering effort goes into ensuring that model can run effectively.
“I was an intern at G-Research before joining full-time in 2022. That intern experience was really good. You get given actual work to do, it’s very interesting. 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.”
“It’s a large intern programme too – I think there were possibly even 20 engineering interns – so you meet people, and many of them are still here.”
"The two biggest things that I like about working at G-Research are the smart and incredibly friendly colleagues, as well as being able to strike a really good work-life balance (in contrast to a lot of the finance industry)."
"There was a lot I didn't know about the G-Research, so I gained insights from those who interviewed me. They all came across as intelligent, curious, and interested in exploring problems from different angles. I figured if people like this enjoy their jobs then I most certainly will."
"G-Research makes a lot of effort to have a very open culture and gives a lot of freedom to its individual researchers to pursue directions that they think are valuable, with each researcher very much driving their own research. I didn’t feel like I was losing a lot of freedom (compared to academia)."
"There are lots of people within the business that have started as a junior and progressed – which I think is testament to G-Research’s belief in fostering growth and recognising potential."
"What I appreciate most about working in G-Research is the supportive and knowledgeable environment. Everyone is incredibly helpful and patient, which ensures there’s a good balance between being challenged and your workload."
Hear from our Head of Forecasting Engineering on why the term "tech debt" has outlived its usefulness. In this blog, he explores why we should move away from generic labels and instead ask more precise, value-driven questions that lead to meaningful improvements in engineering and business outcomes.
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Each month, we provide up to £2,000 in grant money to early career researchers in quantitative disciplines. Hear from our March grant winners.
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Hear from Jay, an Open Source Software Engineer, on tackling technical debt in OpenStack. As technology evolves, outdated code becomes inefficient and harder to maintain. Jay highlights the importance of refactoring legacy systems to keep open-source projects sustainable and future-proof.
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