Cody Jung

Hi! I'm Cody Jung —Building software where AI meets real systems.

Software EngineeringApplied AIData Systems

Computer Science + Business at UC Berkeley. I build AI applications with deterministic checks, resilient backend systems, and measurable impact.

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Selected Experience

Item 1 of 6

Matrix Power

AI Research Intern

Built a FastAPI/PostgreSQL AI sales platform with human-reviewed outreach drafts and HubSpot integration, reducing manual prospect preparation by an estimated 80%. A live-call voice assistant reduced lookup time by 70%; worker leases, bounded retries, and a transactional outbox supported crash recovery and guarded against duplicate actions.

Google

Software Engineer (Contract)

Built a Python/FastAPI and Next.js math tutor with exact-arithmetic checks and resilient PostgreSQL writes. Deterministic checks recorded zero false acceptances and false corrections across 85,942 generated cases evaluated against SymPy; maintained 2,600+ tests in GitHub Actions.

AfterQueryYC W25

Generative AI Engineer (Contract)

Built a Python/LangChain evaluation harness running 35 financial-reasoning benchmarks per model checkpoint. Numerical and schema validators reduced manual scoring effort by an estimated 40% while retaining expert rubric review; failure analysis redirected data collection toward eight underperforming categories.

Haas Business Student Association

Director of Technology

Lead a six-person technology committee shipping React and Next.js features across Berkeley platforms serving more than 6,000 users.

Mastercard

Technology Consultant (Contract)

Modeled customer segments across 990 survey responses and translated the findings into three personalized-rewards product prototypes.

Kinjo Insurance & Financial Services

Software Engineering & Finance Intern

Built a Python ETL pipeline unifying three custodian export formats, cutting report preparation by approximately 40% across 75+ portfolios. Developed a LangChain pipeline that translated daily U.S. and Japanese news and mapped events to portfolio holdings.

Projects & Open Source

Item 1 of 6
ProjectsOpen Source

Government Workflows

Auditable AI finance workflows built for West Covina’s finance department.

Built a finance workflow platform for West Covina’s finance department to address untracked AI public-records risk. A fail-closed pipeline combines deterministic finance logic, source-linked LLM drafts, and audit logs. Eight finance workflows and natural-language Tyler/Munis export search cut ERP lookup time by 60%.

  • Eight finance workflows for recurring municipal tasks
  • Natural-language Tyler/Munis export search — 60% less ERP lookup time
  • Deterministic finance logic with fail-closed AI validation
  • Source-linked LLM drafts for human review
  • Audit logs that preserve an accountable record of each run
Government Workflows preview — auditable, source-linked finance workflows
  • Python
  • FastAPI
  • React
  • TypeScript
  • SQLite
  • Pydantic
View on GitHub (Government Workflows)

College Explorer

Co-founded an AWS-deployed college decision platform serving 7,600 unique users.

Co-founded College Explorer with a three-person team and deployed it on AWS, serving 7,600 unique users. Natural-language college search combines PostgreSQL full-text retrieval, structured filters, and reranking. A weighted ranker compares colleges across six fit dimensions, with data-coverage confidence and sensitivity analysis to make tradeoffs clear.

  • Co-founded with a three-person team; serving 7,600 unique users
  • AWS deployment with PostgreSQL, pgvector, and Redis
  • Natural-language search with full-text retrieval, filters, and reranking
  • Weighted college rankings across six fit dimensions
  • Data-coverage confidence and sensitivity analysis
  • Next.js
  • React
  • TypeScript
  • FastAPI
  • PostgreSQL
  • pgvector
  • Redis
  • AWS
View on GitHub (College Explorer)

AI Backtest Lab

AI-assisted, event-driven backtesting platform with a natural-language strategy builder.

A from-scratch Python backtesting engine wired to FastAPI research APIs, a Next.js dashboard, and LangGraph research agents. Researchers draft strategies in natural language, run parameter sweeps and walk-forward validation, and analyze performance — all behind strict validation boundaries that never execute generated code.

  • Event-driven, bar-by-bar backtesting engine built from scratch
  • Natural-language strategy builder with strict Pydantic validation
  • Research Copilot with explicit approval gates — no code execution
  • Grid search and walk-forward validation workflows
  • 15+ risk/performance metrics (Sharpe, Sortino, max drawdown…)
  • Python
  • FastAPI
  • LangGraph
  • Next.js
  • React
  • TypeScript
  • Pandas
  • Recharts
View on GitHub (AI Backtest Lab)

Vantage

Prediction-market intelligence dashboard correlating price, sentiment, and whale activity.

Vantage answers whether sentiment shifts and large-trader activity lead, lag, or coincide with prediction-market price moves. It ingests market data, detects anomalies, tracks whales, and runs on-demand sentiment analysis, surfacing explainable signals through a FastAPI backend and a React dashboard.

  • Rule-based anomaly detection for price, volume, and liquidity
  • Whale tracking from normalized Polymarket trade data
  • On-demand sentiment analysis via GNews + HuggingFace
  • Correlation view aligning price, sentiment, anomalies, and whales
  • FastAPI read API behind a React/Recharts dashboard
  • Python
  • FastAPI
  • SQLAlchemy
  • React
  • TypeScript
  • HuggingFace
  • PyTorch
  • PostgreSQL
View on GitHub (Vantage)

CombatRL

Deterministic tactical-arena simulator for reinforcement-learning research.

A replay-first, headless tactical combat environment for reinforcement learning and multi-agent behavior research. It runs 2v2 team-aware matches with configurable bot policies, parses natural-language commands into validated behavior profiles, and integrates PPO training through a Gymnasium wrapper.

  • Deterministic headless arena with fixed-timestep physics
  • 2v2 team-aware environment with multiple heuristic baselines
  • Natural-language commands parsed into behavior profiles
  • PPO training via Stable-Baselines3 with checkpointing
  • Replay system with frame/event logs and evaluation artifacts
  • Python
  • Gymnasium
  • Stable-Baselines3
  • PPO
  • Pygame
  • pytest
View on GitHub (CombatRL)

Open Source

  • Ponytail

    Merged POSIX process-group cleanup into agent benchmarks, stopping timed-out process trees.

    View on GitHub (Ponytail)
  • PixelRAG

    Merged auto/MPS CLI overrides into the visual index builder, with pytest coverage for Apple Silicon.

    View on GitHub (PixelRAG)

About

I’m a Computer Science and Business student at UC Berkeley building reliable AI applications and data-intensive software. At Matrix Power, I built AI sales workflows and a live-call voice assistant. As a software engineer on contract with Google, I built a math tutor that pairs an LLM with deterministic checks. I’ve also built financial-reasoning evaluation infrastructure at AfterQuery and co-founded College Explorer, an AWS-deployed platform serving 7,600 unique users. I care about rigorous evaluation, explicit failure boundaries, and turning ambiguous problems into software people can trust.

Pursuing a B.A. in Computer Science and a B.S. in Business Administration at UC Berkeley and the Haas School of Business. GPA: 3.96.

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