Nelson Love — Software QA · Test Automation · Developer Tooling
Correct software, made more humane.
I build the test infrastructure and developer tooling that keep clinical, behavioral-health, and life-sciences software trustworthy — with a background in psychology research and FDA-regulated biotech.
Open to new roles · remote (US)
Selected work
7 entries
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L7 Informatics — SDET
- Python
- LLM function-calling
- Cypress
- test infra
Turned thousands of opaque Cypress failures into structured, queryable data using LLM function-calling — a structured-extraction pattern built before it was standard.
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L7 Informatics — SDET
- Python
- git analysis
- developer tooling
- Bitbucket API
Routed PRs to the right reviewers via git-history analysis with exponential-decay weighting — a legible algorithm that beat the obvious ML approach.
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L7 Informatics — SDET
- Python
- NLP
- 21 CFR Part 11
- regulated SDLC
Mapped 3,000+ tests to 2,500+ requirements for regulated audit traceability, with NLP matching, human-in-the-loop validation, caching, and cost controls.
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HarborView — Independent contractor
- Python
- OpenAI API
- Quickbase API
- SQLite
- data pipeline
A Python tool that profiles manufacturers against a 10,000+ record FDA-compliance database, classifies them with the OpenAI API, and drafts targeted outreach.
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Independent — currently building
- MCP
- Python
- SQLite
- agent tooling
A hosted Model Context Protocol server that safely exposes a 7,000-note knowledge base to AI agents, backed by a typed information model and a SQLite store.
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Independent venture
- FastAPI
- Vue
- Docker
- Terraform
- RunPod
A live audio-processing SaaS — FastAPI and Vue on Docker with Terraform-provisioned GPU inference on RunPod — built and operated end to end.
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Steady — pacing and symptom tracking for chronic illness
In developmentIndependent venture
- product
- health
- measurement
A measurement-driven app for people managing ME/CFS, long COVID, and POTS — pacing and crash-prediction grounded in real psychometrics.
Writing
4 posts
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What the reviewer-recommendation engine taught me about reaching for the legible solution first.
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Using models to turn messy test output into clean, queryable data — the boring, useful application.
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The most reliable place to put a model is between two systems that already know what they want.
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Notes on a compliance-data pipeline — scraping, normalizing, and scoring 10,000+ establishments.
Profile
Background
I started in psychology research — designing studies, running the statistics, publishing on how people find decent work — and moved into software the same way I move through any system: learn how it actually behaves, then build the tooling to keep it honest.
For two and a half years I was an SDET at L7 Informatics, building test automation and CI/CD infrastructure for a LIMS platform used in FDA-regulated labs — working day to day inside 21 CFR Part 11 and GAMP. I also built the developer tooling around it: a reviewer-recommendation engine, an LLM-powered test-failure pipeline, and automated requirements traceability at the scale of thousands of tests.
Since then I've worked independently — building AI-powered developer and go-to-market tooling, a live audio-processing SaaS, the data pipelines underneath them, and infrastructure for AI agents.
What ties it together is a bias toward systems that are both rigorous and humane: software that's correct under audit and clear to the people using it. That instinct is sharpest in clinical, behavioral-health, and life-sciences software — where correctness and human stakes are the same thing.