Jason Bokinz
Full Stack Software Engineer
I graduated from Stony Brook University with a degree in Computer Science and a specialization in Artificial Intelligence. I joined an EdTech startup from day one and helped scale it to 100,000+ users in under two years, owning features end to end across the stack. Instead of being scared of AI like a lot of engineers, I learned how to use it to my advantage, writing the docs, rules, and guardrails that keep tools like Claude Code producing clean, scalable code instead of sloppy work.
Languages
When I'm not coding
Basketball
Lifting
Golfing
Beach
Stony Brook University
Bachelor of Science in Computer Science — Artificial Intelligence
Graduated May 2025
Coursework
Courses That Shaped Me
Systems Fundamentals II
CSE 320 was the hardest class I took at Stony Brook, the kind with a reputation for pushing people out of the major. The work was low-level C and systems programming, and instead of just trying to pass, I pushed for perfection. I tested every edge case I could think of and tried to break my own code before the grader could. That standard showed up in the grades: I scored 100s on three of the projects, where much of the class was in the 40s and 60s, and I finished with an A. But the grade mattered less than what it built in me: staying calm under hard problems, planning my work across long deadlines, and always thinking about what could go wrong before it does.
Natural Language Processing
This is the class that got me into AI. We went deep into how large language models actually work, with hands-on assignments in prompt engineering, text embeddings, retrieval, and why models hallucinate. For the first time I understood what was happening under the hood instead of just using the tools. We also covered different ways to benchmark these models, which is what led me to build my own RAG project outside of class to compare them. It's the reason I do the AI work I do today.
Software Engineering
This was the first class that felt like a real software job instead of homework. We built one project over the semester with a deadline for each phase, designed our screens in Figma first and then built to match them, and worked as a team that split up the tasks. There was no set direction, so we picked our own stack and planned who did what, opened PRs, reviewed each other's code, and worked through merge conflicts. I came out of it knowing how to write code other people can read and how to ship as a team, not just on my own.
Software Development
This is where I first learned front end. We built the same project over and over, starting bare with plain HTML, CSS, and vanilla JavaScript, then leveling up to React and Tailwind so I could see how the fundamentals work before the frameworks hide them. It's also where the bigger picture clicked: the difference between what runs on the server and what runs in the client, how each side handles rendering, and where React actually fits into it. It was the first end to end web app I ever built, and where I got comfortable owning a feature across the whole stack.
Experience
West Palm Test Prep
Software Engineer (Intern → Full-Time, June 2025)
January 2024 - PresentLaunched whiz.study, an AI-powered EdTech platform serving 100,000+ users across web and iOS, owning full-stack development in a TypeScript monorepo (Next.js, React Native, Express, PostgreSQL).
Rebuilt the platform's 300,000+ line monolith into a domain-driven architecture with a 3-engineer team in under 4 months, using Claude Code for large-scale refactoring.
Led a team of 3 engineers to ship an agentic AI tutor driving ~80% of paid subscriptions, guiding students through problems by graphing equations, eliminating answer choices, and surfacing reference material.
Built payment infrastructure spanning Stripe subscriptions, one-time payments, and a custom free-trial entitlement system, processing 1,600+ active paid subscriptions.
Shipped Question Bank, a targeted practice product for drilling specific concepts and question types, built on shared abstractions and a 20-member contract scaffolded across 41 exam modules.
Engineered the codebase for agentic development, authoring architecture heuristics, extension checklists, and CI-enforced architectural rules that enable AI coding agents to make large changes safely.
Projects
Retirement Planning Simulator
Spring 2025A Monte Carlo retirement simulator spawning a parallel Bun worker per path, simulating 40–60 year horizons through a 10-stage annual pipeline into year-by-year success-probability projections.
Implemented financial algorithms including Roth conversion bracket-fill optimization, Required Minimum Distribution calculations, and ordered asset liquidation across 3 account types.
Automated IRS data ingestion with Puppeteer scrapers across 4 IRS sources (tax brackets, deductions, capital gains, RMD tables), persisting year-stamped records to PostgreSQL.
Dynamic Memory Allocator
Fall 2024Implemented a custom dynamic memory allocator in C, replacing the standard library's malloc, free, realloc, and memalign with a hand-built heap manager operating directly on raw memory.
Designed segregated free lists partitioned by size class with a first-fit placement policy, using boundary-tag headers and footers to coalesce adjacent free blocks in constant time.
Engineered block splitting without splinters, enforced 64-byte payload alignment, and validated correctness with a Criterion unit test suite.
RAG Hallucination Benchmarking
Fall 2024A methodology study on reducing LLM hallucination in educational Q&A. Developed an end-to-end RAG pipeline using OpenAI text-embedding-3-large embeddings and Pinecone vector search, grounding GPT-4o-mini responses in retrieved reference material.
Benchmarked zero-shot, few-shot, and chain-of-thought prompting across 1,000+ GPT-4o-mini and GPT-4o responses, scoring factual consistency via cosine similarity against reference embeddings.
Found retrieval grounding improved factual consistency across question types, with chain-of-thought prompting driving the largest gains on reasoning-heavy questions and diminishing returns on stronger models.
HoopMetrics — NBA Team Data Explorer
Summer 2024Built a Python desktop app that scrapes and displays live data for all 30 NBA teams from NBA.com, organized into tabbed views for rosters, player and team stats, coaching staff, and franchise history.
Built a head-to-head player comparison view rendering two players' full stat lines side by side for direct comparison.
Engineered Selenium scrapers pulling multiple live data feeds per team, with a team-ID lookup table to fetch each team's data efficiently.
Let's Work Together
I'm currently looking for full-time opportunities and open to freelance or contract work. If you have a role, a project, or an idea you'd like to discuss, I'd love to hear from you.