Fundamental
AI
DataResearchEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Research Engineer at Fundamental. Skills: machine learning models, data research, data pipelines, data storage. contribute to development of breakthrough machine learning models. identify, characterize and evaluate data sources”
What You'll Achieve.
unlocks trillions of dollars of value by giving businesses the Power to Predict; build technology that transforms how the world's largest companies make decisions
Industry & Context.
What They're Looking For.
Must Have
fundamentals of software engineering
Nice to Have
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, Contributions to open source ML projects
What You'll Do.
contribute to development of breakthrough machine learning models
characterize and evaluate data sources
Building and maintaining ETL pipelines
Designing and implementing scalable
reliable data storage solutions
efficient training pipeline where data is a critical component
How You'll Work.
Team & Collaboration
Collaborating with the rest of the research team; Collaborating with the wider engineering and infrastructure team
Full Job Description
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, 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 MUST HAVE - 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, …) - Fa
Applying for this Data Research Engineer role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Ashby
- Ashby is a fast modern ATS — most applications take under 3 minutes.
- The resume parser is strong; verify parsed experience dates and job titles.
- Custom screening questions are often scored algorithmically — answer completely.
- Location field affects geo-based screening; use your actual metro area.
ANONYMOUS · UNFILTERED
What do employees actually say about Fundamental?
Real rants from real employees. Read before you apply.