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, ...)

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)

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

Benefits

Competitive compensation with salary and equity

Comprehensive health coverage, including medical, dental, vision, and 401K

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

📌 Data Research Engineer (Laza)
🏢 Fundamental
📍 Laza