Phd Research Engineer
2 weeks ago
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 located in London, one of the world’s foremost financial and tech hubs.
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. 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.
Integrating established research into high-value and high-impact applied projects.
Performing applied research to use machine learning (particularly deep learning, transfer learning and few-shot learning) in real world problems.
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.
Familiarising yourself with the current state and performance of our machine learning system in our product implementation together with the technical team.
Taking ownership of the areas of improvement that can be gained in the machine learning system and looking to implement some of the first gains from the list.
Fine-tune one of the newest open-source language models with our data and deploy to test it in our systems.
Proven knowledge of machine learning and/or statistics and demonstrable ability to apply to complex unstructured data
Competency in Python and familiarity with other programming languages such as C++ or Java
Experience with implementing numerical methods and data visualisation
Good knowledge of algorithm design, evaluation and implementation
Ability to adopt an innovative approach to thinking about data in new and creative ways
At least a Masters in machine learning, computer science, mathematics, physics, electrical engineering, or equivalent
Previous experience in Natural Language Processing with large language models
Previous experience leading your own machine learning projects within academia and/or private sector
PhD in machine learning, computer science, mathematics, physics, electrical engineering, or equivalent
Familiarity with AWS, GCP or Azure
A competitive salary (we benchmark with Otta , Machine Learning Engineer or Research Engineer depending on your qualifications)
25 days of annual leave + Bank Holidays
Flexibility with working from home (3 days in, 2 days flexible)
Snacks & coffee at the office
Private healthcare (including dental and vision checks)
Exceptional candidates may qualify for VISA sponsorship.
Link to your LinkedIn page, website or anything else you think might be relevant (unless already mentioned in your CV)
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