“Prior to joining G-Research, I worked as a Software Engineer in the telecommunications industry, where I focused on microservices in billing systems. It was there that I gained my first exposure to enterprise software and the process of building scalable, highly available systems.
“One of my favorite parts about working at G-Research is the nature of the work. My team works on systems that are used by a wide range of individuals, from engineers and researchers to analysts, and it’s satisfying to see how our work impacts our colleagues. The problems we solve are often novel in nature, meaning we get to solve the previously unsolved. I find this to be a great way to stay challenged and engaged!”
"The friendly, collaborative atmosphere here is a breath of fresh air and a perfect fit for me."
"The best thing about working at G-Research is being around such smart people, it motivates you to always want to grow and learn."
"The willingness to collaborate between both teams and functions has made the transition into my new role as easy as possible."
"My team and I have access to a wide range of training opportunities, which allowed us to get the entire team AWS certified within a quarter. We’re actively working on the latest AI and Machine Learning projects to stay ahead of industry standards."
"My favourite part of working for G-Research is that Technology is at the heart of everything we do at the company, driving the business forward and enabling us to stay ahead of the competition."
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 As technology evolves, outdated code becomes inefficient and harder to maintain.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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