About Fundamental

Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is 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

As part of the Research team, you will contribute to ideation, designing, implementing, and evaluating breakthrough Machine Learning models that will be deployed in the real world. Your work will be focused on the entire lifecycle of the models. Alongside the rest of the ML researchers in the team, you will be responsible for our models’ performance in every meaning of this word - whether this means achieving high evaluation scores through novel architectures and training methods, establishing the evaluation protocols themselves, or implementing methods that allow for efficient training and inference. The greatest research is done through solid engineering, so alongside the research you will also take part in ensuring that our research code allows swift, rapid development and testing of new ideas - both your own and the rest of the team’s.

Must have

• Strong familiarity with the full research cycle in Machine Learning

• Strong fundamentals of software engineering

• Strong knowledge of Python, and its ML frameworks

• Experience with:

  • Full lifecycle of AI model development

  • ML infrastructure frameworks and tools

  • Developing new ML methods, algorithms and models

  • GPUs (or TPUs) and distributed training

  • Scaling up models and training regimes

• Knowledge of:

  • Classical ML methods and algorithms

  • Deep Learning techniques

• Expertise in at least one of the following:

  • Tabular models, time series models, relational foundation models, structured data, large-scale foundation models

Nice to have