TL;DR

• We are looking for a Research Engineer or Machine Learning Engineer with expertise in implementing advanced deep learning models, particularly transformer-based models like large language models. The role involves enhancing the performance of our existing models, as well as developing new models using open-source libraries. Strong analytical and machine learning capabilities are essential for success in this position.
• We are looking for someone who can demonstrate that they can be self-directed in the role and be the team’s top expert in their area.
• We value ambition to learn and execute beyond just years of experience.
• The team is growing to ~10 people.
• We are located in London, one of the world’s foremost financial and tech hubs.
• We accept applications from people from any background and are striving to create a diverse and inclusive work environment.

About the role

Our end-to-end data pipeline enables investors to engage in the climate transition through financial instruments more easily and faster than ever. We are building the next generation of interfaces in how capital allocators use AI to interact with the world of sustainability data, like how ChatGPT showed a different way to think about search.

The team is now growing to ramp up building a productised version from our proof-of-concept. We’ve received funding to work on a customer-usable MVP and engaging closely with our users to launch a full version of the product in late 2023. We are looking for a candidate with a background in large language models to productise our current NLP models as well as to develop the next generation of models as the ML community starts to use the learnings from ChatGPT and GPT3.5. The role will be yours to shape with the chance to make a big positive impact on the company and on the world.

This is a hybrid position – a combination of office (we are based in Central London) and remote working.

As a Research Engineer / MLE at ClimateAligned, you’d get to be responsible for:

• Implementing machine learning systems in a variety of environments, particularly in the cloud.
• Integrating established research into high-value and high-impact applied projects.
• Performing research to apply machine learning (particularly deep learning, transfer learning and few-shot learning) to real world problems in NLP.
• Collaborating with Software Engineers to build end-to-end working analytics code, including designing and evaluating new ideas (prototyping) as well as making simple solutions work for customers.
• Reporting and presenting results and findings clearly and efficiently, both internally and externally.

In practice, your job for the first months in the role will involve: