Applied AI Engineer

About FundamentalFundamental 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 responsibilitiesTake part in development and optimization of a large neural network-based tabular model implemented in PythonProfile training and inference pipelines to identify performance bottlenecksRewrite 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 appropriateImprove memory efficiency, latency, and throughput across model pipelinesEnsure correctness, numerical stability, and reproducibility as the model evolvesCollaborate with ML researchers on productionizing new capabilitiesMaintain clean abstractions, comprehensive tests, and clear documentationShape architectural decisions for our ML systems handling tabular dataMust haveStrong software engineering fundamentals with expert-level Python and RustHands-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 productionStrong understanding of neural networksTrack record of optimizing performance-critical codeStrong profiling and debugging skills (CPU, memory, latency)Nice to haveExperience 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 enginesBackground in numerical computing or systems programmingExposure to large-scale ML infrastructure (distributed training, batching, caching)Experience with the Rust async ecosystem (tokio) or SIMD/parallelism crates (rayon, ndarray)BenefitsCompetitive compensation with salary and equityComprehensive health coverage, including medical, dental, vision, and 401KPaid parental leave for all new parents, inclusive of adoptive and surrogate journeysRelocation support for employees moving to join the team in one of our office locationsA mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action