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Quantitative Developer

G-Research is Europe’s leading quantitative finance research firm. We hire the brightest minds in the world to tackle some of the biggest questions in finance. We pair this expertise with machine learning, big data, and some of the most advanced technology available to predict movements in financial markets.

The role

Our Realtime Platform Development (RPD) Group are looking for exceptional software engineers to work alongside our world-class quantitative researchers to help us better predict the future. As part of this select group, you will be responsible for a wide range of projects under the direction of quants at the bleeding edge of research. Previous projects have included:

  • Applying novel machine learning ideas to our datasets using our in-house compute farm
  • Adding new features to open source machine learning packages
  • Extending trading platform capabilities to enable new research
  • Optimise performance critical parts of our trading platform
  • Applying numerical methods to compute solutions to quants’ mathematical models in a latency-sensitive environment
  • Refactoring quant code into high-quality software components with well-defined interfaces and appropriate testing

As an engineer working directly on research projects you’ll have the opportunity to see new research first and be expected to act as a conduit between research and engineering. This involves communicating quant feature requests with sufficient context to ensure engineering projects are triaged and scoped appropriately, and acting as an ambassador for engineering whilst working alongside quants.

Who are we looking for?

The ideal candidate will be an outstanding software engineer, capable of solving a wide variety of technical challenges. They will need to be flexible and pragmatic; comfortable both rapidly prototyping ideas via throw-away code and developing high-quality well-tested components, able to add value through greenfield projects and improvements to legacy code alike.

Although day-to-day research work is often individual, it is always connected to a larger team effort. You must be effective working as part of a team of quants and engineers to improve our trading platform and the research we deploy to it.

The majority of our code is C# or F#, so familiarity with functional programming will be useful. This said, we prioritise the ability to learn whilst delivering value more than experience with any specific language or technology.

The successful candidate will be a software engineer with:

  • Excellent academics such as a 2.1 or better in Computer Science (or similar) from a top university, or as an alternative substantial and highly impressive professional experience
  • Demonstrable ability to engineer high-quality maintainable software and experience with automated testing and continuous delivery
  • Experience working closely with data scientists or quantitative researchers in a research directed environment
  • Experience with machine learning either professionally or from personal projects

Finance experience is useful but by no means a pre-requisite. Candidates from non-financial backgrounds are encouraged to apply.

Why should you apply?

  • Exposure to some of the world’s most foremost quant researchers, working on varied and challenging engineering projects
  • Highly competitive compensation plus annual discretionary bonus
  • Informal dress code and work/life balance
  • Comprehensive healthcare and life assurance
  • 25 days holiday
  • 9% company pension contributions
  • Cycle-to-work scheme
  • Subsidised gym membership
  • Monthly company events
  • Central London office close to 5 stations and 6 tube lines

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.


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