Applied AI Engineer

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

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 Rust (via PyO3 or custom extensions) where Python limits us, with C++ (via PyBind11 or custom extensions) as a secondary option where appropriate

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 Rust

Hands-on experience bridging Python and Rust (PyO3, maturin, or custom extensions)

Working proficiency in C++ and 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 (Rust or C++)

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)

Experience with the Rust async ecosystem (tokio) or SIMD/parallelism crates (rayon, ndarray)

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