Machine Learning Engineer
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
We are looking for exceptional software engineers to work alongside our quantitative researchers on cutting edge machine learning algorithms. As a member of the Data Science Engineering team, you will be engaged in a mix of individual and collaborative projects enabling researchers to tackle some of their toughest problems. In this role, you will use a combination of off-the-shelf tools and custom solutions written from scratch to drive the latest advances in quantitative research.
Past projects have included:
- Implementing ideas from a recently published research paper
- Writing custom libraries for efficiently training on terabytes of data
- Reducing model training times by hand optimising machine learning operations
- Profiling research code to identify performance bottlenecks
- Evaluating the latest hardware and software in the machine learning ecosystem
Who are we looking for?
Candidates will be comfortable working both independently and in small teams on a variety of software engineering challenges with a particular focus on machine learning and scientific computing.
The ideal candidate will have:
- A demonstrable ability to work with the scientific Python ecosystem or other scientific computing languages (e.g. Julia, Matlab, etc.)
- Experience with machine learning either from prior employment, coursework, or personal projects
- Ability to write one-off scripts and unit tested libraries as well as being able to decide when each is appropriate
- An interest in keeping up with new developments in the field including papers and new technologies
- Capability to communicate new and complex knowledge from project work back to the team
Finance experience is not necessary for this role and candidates from non-financial backgrounds are encouraged to apply.
Why should you apply?
Highly competitive compensation plus annual discretionary bonus
- Informal dress code and excellent 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