Jarod ChristiansenFull-Stack Engineer · Frontend-Leaning · Search Systems
I'm a full-stack engineer who leans frontend, with deep experience in search systems. Six years shipping production software across medical, fintech, hospitality, and property-tech. TypeScript and Next.js are home base. ML and AI are what I build for fun on the side.
Scottsdale, AZ · Open to remote, hybrid, or relocation
Experience
Products I've shipped and teams I've led.
- StorableFull-Stack Engineer(Software Engineer II)2023 – Present
Owned frontend systems for a search platform handling 36M annual searches. Led a 5-person engineering team, aligning architecture and shipping optimization work across 3 frontend applications.
- Developed and A/B tested search ranking algorithms for storage facility listings, contributing to $300K/mo in incremental search revenue by improving result relevance based on availability, ratings, and user-filtered criteria across 3 frontend applications.
- Coordinated shared-codebase workflow with 24 developers across time zones, aligning architectural patterns and reducing cross-team integration friction.
- Led and mentored a team of 5 developers on Core Web Vitals optimization across multiple enterprise React applications, improving organic traffic, retention, and WCAG 2.2 accessibility compliance.
- TroopFull-Stack Engineer(Software Engineer II)2022 (6 months)
Joined as founding engineer and built the alpha and beta of a shareholder-activism fintech product from scratch. The company did not raise a Series A.
- Full-stack ownership from day one: Next.js frontend, Node.js API, database schema, and CI/CD pipeline.
- Owned features end-to-end in a fast-moving startup where scope and priorities shifted constantly.
- Choice HotelsFrontend Engineer(Software Engineer I)2021 – 2022
Built and maintained booking UX for a Fortune 500 hospitality platform spanning 7,000+ international properties.
- Developed reservation and booking flows where performance and reliability directly impacted revenue across international markets.
- Collaborated cross-functionally with product and design to iterate on high-traffic user journeys.
Technical Skills
Core Stack
- React 6 yrs · production at scale
- TypeScript 4 yrs
- Next.js 4 yrs
- Node.js 6 yrs
- JavaScript 6+ yrs
APIs & Data
- GraphQL / Apollo
- REST APIs
- PostgreSQL
- MongoDB
- Redis
Frontend Craft
- Accessibility (WCAG 2.1)
- Core Web Vitals
- Styled Components / CSS
- Responsive Design
- Testing (Jest, RTL, Cypress)
Infrastructure
- AWS
- Vercel / Docker
- CI/CD Pipelines
- DataDog / Grafana
- Segment / Analytics
AI & ML (Side Projects)
- Vector Search / Semantic Search
- OpenAI API / LLM Integration
- Python 3+ yrs, ML / data
- PyTorch / scikit-learn
- NLP / text classification
Leadership
- System Design
- Sprint Planning & Agile
- Code Review & Mentorship
- Cross-functional Collaboration
Projects
Side projects where I own architecture, API design, and delivery end-to-end, the kind of full-stack ownership that's hard to show from proprietary work.
Book Recommendation Engine
- Next.js
- TypeScript
- FastAPI
- Zilliz Cloud
- Open Library
- sentence-transformers (MiniLM)
Find your next read from 2.1M+ titles using vector-powered semantic search. Built a Next.js frontend with a FastAPI recommendation API backed by Zilliz, with a three-tier fallback to keep results relevant even for unlisted books.
Key Contributions
- Ingested and embedded 2.1M book titles via a Colab pipeline using DuckDB for deduplication and MiniLM sentence-transformers for vector generation.
- Built a FastAPI recommendation API backed by Zilliz Cloud vector search with a three-tier fallback: stored embedding → live MiniLM inference → subject-filter heuristics.
- Designed the Next.js frontend with Open Library autocomplete, book selection flow, and graceful UX states for each fallback tier.
Real-Time Asset Tracking Platform
- Next.js
- TypeScript
- Apollo (GraphQL)
- MongoDB
- Python
- Redis
Live dashboard tracking 12,500+ assets with real-time updates. Architected a GraphQL API with Redis caching for high-throughput aggregation, served through a modular Next.js frontend.
Key Contributions
- Architected a scalable GraphQL API for efficient real-time data fetching.
- Designed data pipeline and Redis caching layer for high-performance aggregation.
- Built modular Next.js/TypeScript frontend with reusable component library.
MRI Tumor Detection Model
- Python
- TensorFlow
- Keras
- PyTorch
- Matplotlib
- NumPy
Detects brain tumors in MRI scans with precise bounding-box localization. Built with PyTorch and YOLOv8, using transfer learning to maximize precision on limited medical imaging data.
Key Contributions
- Applied transfer learning to boost detection precision on limited datasets.
- Optimized training pipeline for efficient processing of large medical imaging batches.
- Tuned model inference for deployment in both edge and cloud environments.
Interactive Algorithm Visualizer
- Next.js
- TypeScript
- Styled-Components
Step through Merge Sort, Quick Sort, and other algorithms at adjustable speed. Built in Next.js with a modular architecture designed for easy addition of new algorithms.
Key Contributions
- Built an interactive UI that renders algorithm animations smoothly at variable frame rates.
- Used TypeScript and Next.js for type safety and SSR.
- Designed a plugin-style architecture to support adding new algorithms without touching core rendering logic.
Other projects
Financial Forecasting & NLP Suite
- Python
- Pandas
- SkLearn
- Matplotlib
- NumPy
- Keras
- Prophet
Predicts market trends and analyzes sentiment from text using Prophet, LSTM, and scikit-learn. Pulls live data from CoinMarketCap for real-time financial forecasting.
Key Contributions
- Built sentiment analysis and text generation pipelines from scratch.
- Integrated CoinMarketCap API for live financial data ingestion.
- Tuned LSTM architecture and Prophet models for time-series accuracy.
About Me
I started in my tech career in SEO (after being a tattoo artist for 7 years) which sounds like a detour into engineering, but it meant I was thinking about search algorithms and page performance long before I wrote my first React component. That background shaped how I approach frontend work: I default to thinking about what's crawlable, what's fast, and what actually gets found. That usually comes down to elegant, effective UX. It's a weird superpower for marketplace products.
A large portion of my time has been in search and multi-tenant platforms; the kind of systems where a relevance ranking change or a 50ms query improvement shows up directly in reservation rates. I've led a small team, built out design systems, and spent a lot of time on A/B testing infrastructure. That last one sounds less exciting than 'architected a platform,' but it's where the real leverage is when you're coordinating across multiple product teams and need to ship experiments without breaking things.
Outside work I've been building a RAG integrated book recommendation engine backed by vector search, partly because I wanted real hands-on time with embeddings before they became table stakes. I tend to pick up side projects that let me go deep on something I don't often touch in my day job.