San Diego, California / Open to entry-level SWE

Angel Mencia

Full-Stack Software Developer

Entry-level software engineer building database-backed web apps, AI document workflows, and practical ML systems.

Full Stack SWE

Focus

UC San Diego

Education

ML & Neural Computation

Specialization

Angel Mencia

SWE / AI Workflows

FastAPI, Next.js, data systems, testing, and practical AI products.

Project Highlights

Production Grade Software and Projects

Four focused projects that show full-stack engineering, AI workflow design, testing, deployment, applied machine learning, and signal analysis.

Prelegal AI chat interface filling legal document fields with a live preview.

Full-Stack AI App

Prelegal

AI legal document generator with authenticated users, chat-driven field collection, manual fill, live Markdown preview, saved drafts, and PDF export.

FastAPINext.jsTypeScriptSQLiteSQLAlchemyDockerJestPlaywright
GitHubDemo
Document Intelligence Lab outlined PDF layout preview showing detected document regions.

Local-First AI Tool

Document Intelligence Lab

PDF intelligence pipeline that parses documents with Docling, reviews layout items, corrects tables, summarizes visuals, and exports JSON/Markdown/Excel outputs.

PythonStreamlitDoclingOllamapandasopenpyxl
GitHub
Animated Flappy Bird neuroevolution simulator preview.

ML Research Simulator

Flappy Bird Neuroevolution

Headless neuroevolution trainer and Pygame demo for evolving a perceptron policy, with reproducible experiments and PRML-style analysis.

PythonPygameNumPymatplotlibSimulation
GitHubPaper
EEG recording cap used as the visual reference for the eye-state classification project.

Signal ML Analysis

EEG Eye-State Classification

Real public EEG workflow using raw signal inspection, 1-40 Hz bandpass filtering, Welch PSD, spectrograms, window features, model evaluation, anomaly detection, and saved artifacts.

PythonpandasNumPySciPyscikit-learnmatplotlibJupyter
GitHub

Engineering Approach

How I approach engineering

I connect a cognitive science and machine learning background with practical full-stack software work.

I studied Cognitive Science at UC San Diego with a specialization in Machine Learning and Neural Computation, plus a Computer Science minor. That background makes me think a lot about how people reason, how systems learn, and how interfaces can make complicated work feel clearer.

In practice, I use that foundation to build software: full-stack apps, database-backed workflows, document intelligence tools, AI-assisted interfaces, and evaluation-heavy projects where the output needs to be understandable and useful.

Explore the full expertise map

Each area is a way I turn background knowledge into working software: full-stack systems, structured data flows, applied AI, and careful technical evaluation.

Tech Stack

Tools I use to build

A practical stack for full-stack web apps, AI document workflows, data pipelines, testing, and deployment.

Languages

Python
TypeScript
JavaScript
Java

Frontend

React
Next.js
Tailwind CSS

Backend

FastAPI
Node.js
REST APIs
SQLAlchemy
JWT / HttpOnly auth

Databases

PostgreSQL
SQLite

DevOps & Testing

Docker
Git
GitHub
Jest
Playwright

Data & AI

Streamlit
pandas
NumPy
SciPy
scikit-learn
matplotlib
Jupyter
LangChain
RAG
OpenAI / Anthropic APIs
Docling
OCR / layout parsing

Experience

Software experience & education

Recent engineering work, technical ownership, and the academic foundation behind the projects.

CurrentJan 2026 - May 2026

Software Developer Intern

Millenia Ventures

Software Engineering / Data Automation

Built the first structured technical system for an AI fundraising intelligence platform, spanning PostgreSQL schema design, AI/OCR extraction, validation workflows, dashboard flows, and stakeholder review.

  • Designed PostgreSQL-backed workflows for founder records, investor records, source documents, extracted fields, review status, correction states, and dossier outputs.
  • Automated review of 1,000+ founder/company source files, cutting manual document review from 5 hours to 20 minutes through structured JSON extraction and source evidence tracking.
  • Built an investor-founder matching algorithm that surfaces 50,000+ investor candidates using spreadsheet ingestion, business-data APIs, semantic embeddings, filters, keyword overlap, and weighted scoring.

Education

Sep 2021 - Mar 2026

B.S. Cognitive Science

UC San Diego

Specialization: Machine Learning & Neural Computation

Sep 2021 - Mar 2026

Minor in Computer Science

UC San Diego

Coursework

Machine Learning, Data Science in Practice, Neural Signal Processing, Probability & Statistics, Linear Algebra, Data Structures & Algorithms, and Business Analytics.

Contact

Let's build something useful

Open to entry-level software engineering roles, internships, and teams building data-heavy or AI-assisted products.