TL;DR

• We are looking for a Machine Learning Engineer or Research Engineer with expertise in deploying deep learning models, particularly transformer-based models like large language models. The role involves enhancing the performance of an existing machine learning system (including fine-tuning the underlying models) through various improvements across the system. 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.
• 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

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. Our end-to-end data pipeline enables investors to engage in the climate transition through financial instruments more easily and faster than ever.

The team has built a productised version from our proof-of-concept and are looking to continue developing our machine learning capability and enable new workflows for our users. We are looking for a candidate with a background in large language models to take ownership of the machine learning system performance through engineering modifications, prompt engineering, fine-tuning and by creating a system of models to enable the utilisation of the new generation of models in actual workflows. 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 – You’ll be expected to be in the office at least 3 days a week.

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.
• Performing applied research to use machine learning in real world problems. In particular your work will focus on modern LLMs (both api and in-house) and traditional NLP-focused deep learning, transfer learning, and few-shot learning.
• Integrating established research into high-value and high-impact applied projects.
• 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:

• Familiarising yourself with the current state and performance of our machine learning system in our product implementation together with the technical team.