Machine Learning Engineer

About Us Theia Insights is a venture-backed deep tech company. We build future-proof AI solutions in Industry Classification, Risk Factor Models, and Portfolio Analysis for the global finance and investment arenas. We are a team of PhD scientists, engineers and mathematicians, with decades of combined experience. Our products and solutions are built upon a foundation of academic and proprietary research and the latest developments in AI, machine learning, Natural Language Processing (NLP), and Large Language Model (LLM) technologies. We are guided by our commitment to sustainably building incredible technology to better serve the needs of a rapidly evolving world and ever-changing investment landscape. We are on a mission to leverage artificial intelligence and revolutionise how information is transformed into insights. Your role We are seeking a highly talented ML Engineer to contribute to our success in building the core models for the investment research technologies of tomorrow. You' ll be part of a growing team (reporting to the COO) bringing revolutionary AI advances in Natural Language Processing (NLP), including developments in large language models (LLMs), generative AI, data extraction, and knowledge graph construction for the financial industry. We are specifically looking for a machine learning engineer with at least one years experience building and deploying models to a production environment Your responsibilities Research and implement state of the art LLM and NLP algorithms for knowledge graph construction, including information extraction, event detection, summarisation and thematic clustering. Run and interpret the results of NLP experiments, including data collection, cleaning, model training and evaluation. Productionise code as part of an engineering team, which includes writing tests, ensuring reliability and repeatability, and making it operationally ready to run in the cloud. Collaborate with financial mathematicians and economists to integrate NLP and knowledge graph components into innovative products and customer solutions. Make informed decisions on technology, data sources, and algorithms to influence system design and product vision. Our tech stack Machine Learning and Data Pipelines : Python and HuggingFace are used for the machine learning aspects of the technology. Front End : TypeScript and React are used for creating the user interfaces. Data Storage and Management : Amazon AWS stack. Data is stored in AWS S3 buckets. J ob Orchestration Framework : We use Dagster for managing and orchestrating jobs. Who you are Essential: 1+ year of with production experience on a Machine Learning or Natural Language Processing focused product 3+ years experience with Python and deep learning frameworks (e.g. PyTorch, Tensorflow) Youve already built and trained models yourself (ie you can take a data set and create a model based on that data) You believe in writing your own automated tests Willing to work as part of a multidisciplinary team in a fast-changing start-up environment Our culture is integrity, humility, and pursuit of excellence. We are looking for candidates who resonate with these principles, and has a passion for technology innovation and desire to build what is best for customers. Desirable: Master's degree or higher in CS, ML, Math, or related field experience Demonstrable record of achievement (e.g. publications, open source contributions, projects) in Machine Learning and NLP Experience with applying and fine-tuning large language models (other than OpenAIs) to business settings Experience with designing and implementing batch and real-time data pipelines Experience with Amazon Sagemaker, Weights and Biases, or similar MLOps tooling Knowledge in finance and economics Interview process Round one: Virtual values based interview with our talent team (Berg Search) Round two: Virtual technical interview with one of our team Round three: A short take home assessment (paid) Round four: A final on-site interview Offer! Details Full time hybrid role Our office is in Cambridge and we would expect you to work in the office two/three days per week. However, if this isn't possible but you believe yourself to be an ideal match, please get in touch!