About the role
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 Take part in development and optimization of a large neural network-based tabular model implemented in Python Profile training and inference pipelines to identify performance bottlenecks Rewrite critical components in C++ (via PyBind11 or custom extensions) where Python limits us Improve memory efficiency, latency, and throughput across model pipelines Ensure correctness, numerical stability, and reproducibility as the model evolves Collaborate with ML researchers on productionizing new capabilities Maintain clean abstractions, comprehensive tests, and clear documentation Shape architectural decisions for our ML systems handling tabular data Must have Strong software engineering fundamentals with expert-level Python and C++ Hands-on experience bridging Python and C++ (PyBind11, Cython, or custom extensions) Experience developing and maintaining ML models in production Strong understanding of neural networks Track record of optimizing performance-critical code Strong profiling and debugging skills (CPU, memory, latency)
Nice to have Experience with tabular ML approaches (transformers, tree/NN hybrids, learned embeddings) Familiarity with PyTorch internals or writing custom ops Experience optimizing training loops, data pipelines, or inference engines Background in numerical computing or systems programming Exposure to large-scale ML infrastructure (distributed training, batching, caching)
