Applied AI EngineerAI

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)

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)

Working proficiency in C++ and experience bridging Python and C++ (PyBind11, Cython, or custom extensions)

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