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)
Benefits
Competitive compensation with salary and equity
Comprehensive health coverage, including medical, dental, vision, and 401K
Fertility support, as well as 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
