Fundamental develops NEXUS, a Large Tabular Model that converts enterprise structured data into highly accurate predictive insights.
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. ABOUT THE ROLE In this role, you'll research, develop, and productize data science capabilities that enhance and expand our product performance on real enterprise use cases - working across a wide range of prediction tasks, data types, and business domains. You'll go deep on hard data science problems, collaborate closely with R&D on product capabilities, and ship production-grade work that has a direct impact on Production use cases. KEY RESPONSIBILITIES - Research and develop data science methods that improve NEXUS predictive performance across diverse enterprise datasets, industries, and prediction task types - Design and implement robust, production-quality Python components with a strong focus on correctness, generality, and reusability - Deeply understand the characteristics of real-world enterprise data and develop strategies that help NEXUS handle them reliably - Run rigorous experiments to measure the impact of new approaches, design meaningful benchmarks, and use results to guide prioritization - Work across a wide variety of structured data problems - including but not limited to classification, regression, ranking, and forecasting - Collaborate closely with the Engineering and Research teams to develop a deep understanding of NEXUS model behavior and use that knowledge to inform your work - Work with Applied AI Engineers to validate approaches on real customer datasets and translate findings into product capabilities - Contribute to technical documentation and internal best practices, helping the broader team apply new capabilities correctly and confidently MUST HAVE - 5+ years of experience in data science or machine learning roles - Strong Python skills, including fluency with pandas, numpy, and scikit-learn - Deep hands-on experience with traditional ML models: XGBoost, LightGBM, CatBoost, and similar gradient boosting frameworks - Solid understanding of what makes real-world tabular data challenging: class imbalance, high cardinality, distribution shift, missing values, and more - Strong experimental mindset - comfortable designing benchmarks and drawing rigorous conclusions from noisy results - Ability to work autonomously and drive work from idea to shipped output NICE TO HAVE - Familiarity with tabular foundation models (TabPFN, CARTE, or similar) - Competitive data science experience (Kaggle, DrivenData, or similar) - especially top finishes on tabular competitions - Background in a domain where structured prediction matters: finance, supply chain, healthcare, retail, or industrial - Experience contributing to or designing internal ML libraries or shared tooling - Familiarity with DuckDB, Polars, or modern in-process analytics engines - Comfort reading ML research papers and translating findings into practical implementations BENEFITS - Competitive compensation with salary and equity - Comprehensive health coverage for you and your dependents - Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys - Relocation support for employees moving to join the team in one of our office locations - A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action