G-Research associe recherche quantitative et ingénierie informatique pour analyser les marchés financiers.
Machine Learning Engineer Internship
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…
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Détails de l’offre
La description complète publiée par G-Research.
Description de l’offre
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. Role purpose Dates: 28th June 2027
- 17th September 2027 Working hours: 09:00
- 17:30 We are looking for exceptional machine learning students to join our world-class technology teams for a 12- week summer internship, working alongside quantitative researchers on cutting-edge machine learning problems. As an intern within the Technical Machine Learning team, you will be engaged in a mixture of individual and collaborative work to tackle some of the toughest research questions. You will also use a combination of off-the-shelf tools and custom solutions written from scratch to drive the latest advances in quantitative research. Key Responsibilities of the role include What does the technical machine learning team work on?
- Implementing ideas from published research papers
- Writing custom libraries to efficiently train models on petabytes of data
- Reducing model training times by hand optimising machine learning operations
- Profiling custom machine learning architectures to identify performance bottlenecks
- Evaluating the latest hardware and software in the machine learning ecosystem Who are we looking for? We are looking for interns that 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 current undergraduate, master's or PhD student in machine learning or a related discipline
- Strong object-oriented programming skills and experience working with Python, PyTorch and NumPy are desirable
- Experience in one or more advanced optimisation methods, modern ML techniques, HPC, profiling, model inference; you don’t need to have all of the above
- Excellent machine learning reasoning skills, with the ability to develop your own models when standard approaches are insufficient
- Strong communication and collaboration skills, with the ability to work effectively in a team with complementary expertise Finance experience is not necessary for this role and candidates from non-financial backgrounds are encouraged to apply.
Why join us
- Highly competitive compensation
- Lunch provided (via Just Eat for Business ) and dedicated barista bar
- 30 days’ annual leave pro rated
- 9% company pension contributions
- Informal dress code and excellent work/life balance
- Opt-in private health insurance
- Monthly company events
- Centrally located office (close to five stations and six 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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