About the role
About Fundamental Fundamental is an AI company pioneering the future of enterprise decision-making. Founded by DeepMind alumni, Fundamental has developed NEXUS – the world's most powerful Large Tabular Model (LTM) – purpose-built for the structured records that actually drive enterprise decisions. Backed by world class investors and trusted by Fortune 100 companies, Fundamental unlocks trillions of dollars of value by giving businesses the Power to Predict. At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join
the team defining the future of enterprise AI. Key
responsibilities The greatest research is done through solid engineering. As part of the research team, you will contribute to development of breakthrough machine learning models by working on one of the most crucial aspects of ML model training: data . The main
responsibilities of this role are: Helping to identify, characterize and evaluate data sources Building and maintaining ETL pipelines Designing and implementing scalable, reliable data storage solutions Collaborating with the rest of the research team to maintain a reliable, efficient training pipeline where data is a critical component Collaborating with the wider engineering and infrastructure team Must have Experience with: Identifying good data sources to train and evaluate ML models Bringing data from structured and unstructured sources into formats accessible by ML models Strong fundamentals of software engineering Strong knowledge of: Python Python data processing stack (numpy, pandas, …) Familiarity with: distributed processing (e.g. Ray, Dask Spark, Beam) data storage solutions Basic ML knowledge
Nice to have Contributions to open source ML projects BSc/MSc/PhD in computer science/machine learning Experience working with tabular data / predictive analytics Experience working with "classical machine learning and deep learning" (pre-LLM)
