Data Research Engineer

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.

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, including realistic synthetic data generated from Structured Causal Models and physical / systems-based simulators

• 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

• Experience with:

  • Identifying good data sources to train and evaluate ML models, including real-world and realistic synthetic data sources

    Bringing data from structured and unstructured sources, as well as simulators and causal models, 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

• 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)

• Experience working with synthetic data generation, Structured Causal Models, or physical / systems-based simulators

• 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