Nathan Baune
Platform & Systems Engineer
I build research and development infrastructure so that we can spend our time on the things that need human judgment — creativity, deep thought, asking new questions — instead of fighting tools to arrive at answers.
Over fifteen years I've done this across software and hardware in neuroscience and biology: real-time closed-loop BCI systems, GPU-scale biological simulation, neuroimaging pipelines, multi-language scientific compute platforms. The goal is humane, efficient workflows that let us explore more, dig deeper, iterate faster.
The pattern is consistent: identify fragmentation, design declarative interfaces, add smart orchestration and tracking. The tools I use are ML, AI, modern compute (GPU/CPU and distributed), databases, and accessible interfaces (reducing the cognitive burden and intimidation that keeps capable people working with complex methods and in domains that might benefit from their input).
Core projects spanning ML systems, research infrastructure, and platform engineering:
Technologies
Current Interests
I'm actively exploring the intersection of AI and complex systems:
- Agentic AI architectures — how multi-model orchestration, tool use, and planning emerge in LLM-based systems
- Interpretability and alignment — making AI behavior legible and predictable, from SHAP/LIME to mechanistic interpretability
- Simulation as testbed — using MUSE's transparent, deterministic systems to study how architectural choices shape emergent behavior
- Human-AI collaboration — designing interfaces where AI augments rather than replaces human judgment (see: BABEL, Epoche)
Read more: How this question connects everything I've built →
Platform & Build Engineering
Build, codegen, packaging, runtime infrastructure. Click a thumbnail to view it below.
—
Research Tools, Lab Infra & Applications
ML / signal-classification apps, real-time lab signal infrastructure, and research-support utilities.
—
Recent Writing
What I Learned from Years Working with "Black Boxes"
Build for inspection. Make state observable. Use AI for what it excels at.
Why ML Interpretability Needs Storytellers
Epoche: making ML legible to domain experts, not just engineers.
The Divide Between Deterministic Simulation & Black-Box AI
Why MUSE uses LLMs for translation, not generation.
From Research Pipelines to Production
Lessons learned from years operating across different engineering worlds.