Nathanial Hapeman

HRL is a world leader in silicon-based spin qubits for quantum computing. I help in many ways, primarily serving as lead architect and developer of a Python-based testing framework. It is used on both new and existing systems for fully automated whole-wafer and packaged-device testing.

• Lead developer on Python-based automated wafer probing framework
  • Doubled throughput on legacy systems while achieving near-theoretical runtimes on newer systems
  • Designed centralized control architecture enabling single-host operation of multiple probe stations
  • Built a hardware abstraction layer with instrument mocking for full CI/CD testing and rapid iteration
  • Partnered with physicists to qualify hardware, explore results, and design scalable test architectures
  • Implemented Pydantic-driven configurations, Typer-based CLIs, and MS SQL + HDF5-backed data pipelines
• Fullstack developer on real-time data acquisition and visualization platforms for quantum-relevant data
  • Created a universal, cross-network device visualizer for semiconductors using WKB polygons from a database
  • Built Monaco-based REPL frontend and Python backend with custom data serialization for experiment control
  • Implemented real-time plotting and analysis tools using Plotly, WebSockets, Vue, and IPC
  • Created tools to visualize device yield by overlaying database-aggregated test results on devices
• Recognition
  • Recipient of the 2024 HRL Award for 'Work Excellence by an individual or Team on Critical Programs'
  • Promoted off-cycle twice

Applied Invention partners with large enterprises to design and deliver solutions to complex technical challenges. Most of my work can be summarized in these three projects:

• Computer Vision Aquaculture Project
  • Lead architect and developer on image annotation website using Angular, Flask, Postgres, AWS
  • Worked extensively with annotation team to improve the website user experience
  • Imported and cleaned training data from third party sources while working with internal annotators
  • Built dashboard to monitor and control image pipeline, track annotator performance, inspect anomalies
  • Developed multiple computer vision solutions like multi-label recognition, quality filtering, image scoring
  • Core contributor to a ML data pipeline built on Pachyderm, AWS, and Kubernetes, operating at ~400 pods
• Rental Forecasting Project
  • Added to team to resolve performance bottlenecks and expand forecasting system to nationwide scope
  • Delivered a 50x performance improvement through query optimization, parallel execution, memoization, profiling, and migrating hot paths to C++
  • Automated model accuracy checks and used slack bots to report discrepancies and result summaries
• Geospatial Simulation Development
  • Worked on several GIS projects using Cesium.js, UE4 based and in-house engines
  • Wrote entire file system diagnostics website for a map server
  • Added support for dynamic terrain layer switching and wrote custom shaders
  • Built a hotkey menu system that allows users to customize and define new hotkeys