I'm an expert in applied science, machine learning engineering, and causal inference.

Right now, I’m especially interested in:

• agentic development systems
• scientific tooling and research infrastructure
• applied AI for substantiation, robotics, and materials science

• Building systems that make AI agents more deliberate, verifiable, and useful
• Agentic workflows for science, engineering, and discovery
• Translating causal and statistical thinking into practical software

A framework for deliberate, spec-driven, verification-first software engineering with specialized agents and quality gates.

A toolkit for computational materials science workflows, including domain-specific skills and Materials Project / MPContribs integration for Claude Code.

A Python library for econometrics and causal inference on Polars DataFrames, with a Rust backend for performance.

I lead applied science and machine learning engineering work at Amazon, and my background is in causal inference and large-scale applied ML.

• Website: nordlund.ai
• LinkedIn: linkedin.com/in/jamesnordlund
• X: x.com/JamesNordlund