Available for new opportunities

Hi, I'm
Emmeline.

I build

Software Engineer shipping LLM-powered products at Readgates. I architect the kind of systems that handle millions of requests, reason over documents in milliseconds, and collaborate in real-time — without breaking a sweat.

4+ Companies
~200ms RAG Latency
99.5% Task Reliability
Query Speedup
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About

The person behind the code

Full-stack engineer with a deep interest in distributed systems and AI infrastructure.

I'm Yiren (Emmeline) Xu — a Software Engineer at a startup, where I spend my days building RAG pipelines, designing real-time collaboration systems, and making LLM inference actually fast. It's the kind of work that sits at the intersection of AI and backend engineering, and honestly, I wouldn't have it any other way.

I completed my Master's in Software Engineering at UC Irvine in 2025, where I got obsessive about distributed systems, concurrent programming, and software architecture. Before that, a dual BS in Computer Science & Accounting at Kean University — yes, the accounting degree comes in handy when pitching the ROI of a refactor to stakeholders.

I care about code that's readable, systems that are observable, and products that people actually enjoy using. Outside of engineering: miHoYo (GenShin, 3Z, Star Rail), Reading, Cooking.

Quick Info
📍
Location
San Francisco, CA
🏢
Current
Software Engineer @ Stealth Startup
🎓
Education
MS Software Engineering
UC Irvine · 2025
✉️
Email
emmelinexu23@gmail.com
Experience

Where I've built things

From AI-powered platforms to healthcare apps to urban data systems.

Readgates
Now Jun 2025 — Present
Software Engineer
San Jose, CA
TypeScript Next.js Nest.js MongoDB Kafka AWS Lambda WebSocket RAG
  • Built an LLM-powered RAG pipeline with document chunking, vector embeddings, and MongoDB sharding — enabling semantic search across user docs at ~200ms retrieval latency.
  • Designed a real-time collaboration layer using WebSocket + event-driven state management, enabling AI-assisted co-editing and cutting frontend latency by 20%.
  • Built a distributed media processing pipeline via Kafka Streams + AWS Lambda — async processing, live progress tracking, 99.5% task reliability.
  • Deployed LLM inference services with Ray-style pipeline orchestration and request batching to scale AI features without blowing up costs.
EcoMetricx Sep – Dec 2024
Software Engineer Intern
Irvine, CA
TypeScript React.js Apache Flink Snowflake Redis Cluster Prometheus Grafana Azure
  • Built a scalable ETL pipeline with Apache Flink + Snowflake for energy data processing; materialized views + partitioning delivered a 3× query performance boost.
  • Shipped an NLP service backed by the Gemini API + Redis Cluster caching, hitting sub-200ms latency under 50+ concurrent requests.
  • Implemented full observability (Prometheus + Grafana) across the stack, deployed on Azure VM — 99.9% uptime, zero guessing when something breaks.
Lavender Lotis May – Aug 2024
Software Engineer Intern
Washington, DC
React Native GraphQL Apollo Server AWS PostgreSQL Redis Kafka
  • Built a healthcare app in React Native + GraphQL/Apollo with an AI chat interface and real-time appointment updates via WebSocket.
  • Designed a two-tier caching system (Redis Cluster + in-memory local cache) for microservices, dramatically cutting redundant DB queries.
  • Implemented distributed transaction management using Kafka + local message table; PostgreSQL schemas with table partitioning to handle appointment scheduling at scale.
Insigma Technology Aug – Sep 2023
Software Engineer Intern · Backend
Hangzhou, China
Java Spring Boot MySQL Redis MyBatis JUnit
  • Developed Spring Boot microservices for a city management platform handling geospatial data — Redis caching + spatial indexing pushed it to 500 QPS.
  • Built RESTful APIs with MyBatis for complex geospatial ops; custom indexing + caching strategies trimmed read latency by 30%.
Projects

Things I've shipped for fun

Hackathons, side projects, and experiments in AI + systems.

🍳
🏆 Irvine Hackathon 2025
AI Recipe Recommendation Engine
  • LLM-powered ingredient extraction feeding a lightweight RAG pipeline with vector search — the fridge talks, the model listens.
  • Agentic workflow where an LLM orchestrates ingredient parsing, recipe retrieval, and ranking to generate step-by-step cooking reasoning.
  • Data pipeline for structuring recipe datasets and generating embeddings for semantic search across 10k+ recipes.
  • LLM inference APIs deployed on AWS EC2 + Vercel with request batching and streaming responses for snappy UX.
React.js Node.js Express.js MongoDB AWS EC2 RAG LLM Agents Vector Search
Skills

The toolkit

Languages, frameworks, infrastructure — the full stack, literally.

🤖 AI / LLM
RAG Pipelines LLM Inference vLLM Vector DBs AI Agents LangChain Claude Code
⚙️ Backend
Java Python Node.js Spring Boot Express.js Nest.js GraphQL gRPC
🖥️ Frontend
TypeScript React.js Next.js Redux TailwindCSS Jest Cypress
☁️ DevOps / Cloud
Docker Kubernetes AWS GCP Jenkins CI/CD Prometheus Grafana
🗄️ Database / Cache
PostgreSQL MongoDB MySQL Redis Elasticsearch Supabase
Education

Where I learned to think

Two degrees, one obsession: building systems that actually work.

Master's Degree
MS in Software Engineering
Donald Bren School of Information & Computer Sciences
GPA 3.93 / 4.0
Rank Top 5%
Years 2023 – 2025
Bachelor's Degree
BS in Computer Science & Accounting
College of Science, Mathematics and Technology
Rank Top 10%
Years 2019 – 2023
Contact

Let's build something

Whether it's a full-time role, a project collaboration, or just a good conversation about distributed systems — my inbox is open. I reply fast.