Senior Machine Learning Engineer About Groundtruth AI Groundtruth AI was founded in 2024 are just about to celebrate our first year. We've grown from 2 people to 4 in the last year and have made considerable progress already. We are a Google Cloud partner working to help major financial institutions transform the way they find and fight financial crime. Our founders have worked with Google for years and were key figures in shaping and building Google’s latest Cloud product targeting Anti-Money Laundering. We exist to deploy technologies that make a measurable difference in tackling financial crime. The billions of dollars stolen and laundered each year mask untold human suffering which we can help prevent. Our founders have worked with Google for years, and Groundtruth is a Google Cloud Platform partner working to help major financial institutions transform the way they find and fight financial crime. The billions of dollars stolen and laundered each year mask untold human suffering. We exist to deploy technologies that make a measurable difference in tackling these problems. Who are we looking for? We are looking for machine learning engineers to build and deploy repeatable data and machine learning pipelines, webapps and end to end systems for AI products on our banking client’s GCP infrastructure. You’ll be involved in defining and automating with diverse datasets as you explore and understand the data and domain. We are a young company and you will be working with the co-founders from the very start. You will have a demonstrable track record of getting things done in environments where the objectives are sometimes ambiguous. You will be comfortable with working with novel technologies and techniques as you go along, and owning a problem from end to end. We are strong believers in high quality software delivery alongside and an engineering led approach to consulting. You don’t need to be an expert in financial crime, but you do need the intellectual curiosity to learn more. Experience These experience levels are a minimum and we’re recruiting across a range of experience levels for the right person. 3 years experience of: Delivering software into production environments with an emphasis on data processing or MLOps. Working as part of a development team with version control technologies. Experience developing data transformations on large scale data platforms, either relational or non-relational. Ad-hoc data analysis and data exploration Experience debugging data processes, resolving and articulating problems with data and performance optimization. Solving and implementing practical strategies for system and architecture design, preferably within financial services or another complex or regulated industry. Desirable Experience of financial crime and transaction monitoring Experience of working with managed machine learning APIs. Tech stack We expect to test some of these during the interview process. Must: Proficiency with python in an organized code base for data pipelines and machine learning. Proficiency with data manipulation languages and carrying out data analysis and hypothesis testing - Advanced SQL OR python Experience with "big data" technologies and data platforms - we use bigquery, apache ibis, sqlglot, DBT. You might have experience with hadoop, hive, redshift, snowflake, spark or similar. Experience with Version control/CI/CD - we use git and github actions. Fluency with unix or macos shells, ssh Shell and docker Unix/Docker - Data platforms, e.g. cloud or hadoop - Google Cloud Platform, AML AI Desirable : API based machine learning solutions - we use Google's AML AI. Other "Full-stack" experience, particular with webapps - react, next.js Responsibilities Lead deployment models and solutions onto client environments by transforming and exploring client data on their systems. Drive the development of robust, repeatable and deployable data and MLOps pipelines to tune, train and predict. Creatively adapt to a range of different client technologies. Working with the co-founders, prioritise and implement additional data and features to improve our success metrics. Education Quantitative ability, either through a formal education in a quantitative subject or equivalent experience. Understanding, designing and articulating how to evaluate models is a part of the role. Language English fluency essential Benefits Hybrid Working - 2/3 days in the office in London. £65k-£95k Pension contributions - 3% contribution match Bonus up to 15% of base salary, dependent on company performance 25 days holiday Visa Sponsorship We regret that we do not currently hold a Visa Sponsorship Licence though we are continuing to apply for one.