Natural Language Processing 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 our teams' most powerful ideas.

Join a research team where curiosity meets scale. You'll investigate foundational questions, uncover market insights and push the boundaries of what's possible - all with the support of near-limitless compute and world-class peers.

Take the next step in your career.

• 10-week summer programme (22nd June to 28th August 2026)

• 09:00-17:30 working hours

• Based in Central London

Over the course of 10 weeks, G-Research Summer Research Programme interns gain a unique insight into life as a Natural Language Processing (NLP) practitioner at a leading quantitative research firm.

Our full-time NLP researchers use a wide range of tools and techniques in an applied setting, putting their expertise to use in direct, production-ready applications with immediate results. They have access to vast computing resources and are limited only by their imagination.

As an NLP intern, you will have the opportunity to experience this as part of a 10-week programme working on a meaningful and challenging research project that demands the application of innovative yet pragmatic mathematical and computational analysis.

You will be paired with a mentor who will supervise your work and provide ongoing feedback to help you improve and develop, as well as access to senior staff who are leaders in their fields. Your internship will culminate in a final presentation of your research ideas to senior management.

Taking part in G-Research's Summer Internship Programme will give you an in-depth insight into our academic approach to the world of quantitative research and allow you to explore the thriving city of London, while you get to know your fellow interns and colleagues through a full itinerary of fun social events.

Top performers on the programme will be considered for full-time opportunities on completion of their studies.

The ideal candidate will, at a minimum, have experience in the following areas:

• A post-graduate degree in Natural Language Programming or a related discipline, or commercial experience developing novel machine learning algorithms. We will also consider exceptional candidates with a proven record of success in online data science competitions, such as Kaggle. PhD level study is preferred

• Experience working with large-scale, noisy text datasets and building models for information extraction, sentiment analysis or semantic understanding

• Publications at top-tier NLP or machine learning venues, such as ACL, EMNLP, NAACL, NeurIPS and ICML are advantageous

• Strong coding skills in Python and experience with modern ML frameworks, such as PyTorch and TensorFlow

• Excellent reasoning skills and mathematical ability are crucial: off-the-shelf methods don't always work with our data, so you will need to understand how to develop your own models

Previous experience in finance is not required, although an interest in finance and the motivation to rapidly learn more is a prerequisite for working here.

• Highly competitive compensation plus accommodation

• G-Research community with weekly intern activities

• Lunch provided (via Just Eat for Business) and dedicated barista bar

• 30 days' annual leave pro-rated

• Informal dress code and excellent work/life balance

• 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