non-global max :(
JD Krasnick
AI engineer and researcher building reliable agents, evaluation systems, and learning tools that hold up on real-world data.
A builder who thinks in models.
I'm an AI engineer and researcher studying Computer Science and Operations Research and Engineering at Cornell. I turn messy data and black-box models into systems that are observable, testable, and reliable enough to trust in production.
My work spans production LLM evaluation, agentic workflows, deep reinforcement learning, and the infrastructure needed to make each one measurable.
When I'm not coding, you'll find me out running on a trail, in the gym, or halfway through the latest YouTube video essay that wandered into my feed.
Building systems that hold up in practice.
AI Engineer Intern
Order.co- Built AI systems for product-offer ingestion and location-aware pricing
- Developed LLM evaluation workflows using production traces and analyst dashboards
- Created browser-interaction agents for dynamic vendor websites
Head of Research
Generative AI at Cornell- Leading research on agent-harness optimization
- Built AI workflows for ESG monitoring and financial analysis
- Developed LLM-based data pipelines and analyst tools for citation-backed insights
Financial Software Engineer
Cornell FinTech Club- Developed loan-approval workflow software with Next.js, FastAPI, and PostgreSQL
- Built configurable credit rules and ML models for default-risk scoring
- Automated document extraction and audit logging for credit decisions
Undergraduate Researcher
Cornell University- Researching routing and scheduling in multiclass queueing networks, advised by Professor Dai
- Researching deep reinforcement learning for LLM inference optimization
- Built discrete-event simulators and Gym-style environments for queueing-network research
Data Engineer
University of Pennsylvania Law Professor- Built data pipelines from websites and PDF documents
- Cleaned and structured research datasets for analysis
Selected Work
Evaluation systems, adaptive-model research, and production software built around measurable reliability.
EvalBudget
Adaptive evaluation toolkit for paired model outputs that uses anytime-valid confidence sequences to stop once the evidence is decisive, while preserving raw outputs, grader configuration, and category-level results for auditability.
Agent Arena
Adversarial verification harness that ran 20+ PR-defense matches across Claude Code, Codex, and Gemini CLI, promoting only four-run reproducible test attacks and preserving resumable evidence across up to five attack-repair rounds.
Alloc
Local financial-intelligence agent that turns 36,000 transaction-level records across three companies into cited insights and forecasts, with 39–71 ms retrieval and deterministic, crash-safe proposal triage.
Ntern
Open-source opportunity radar for technical internships and early-career roles, designed to surface credible openings and take candidates to the employer’s official application.
Discrete Event Simulator
Interactive queueing-network simulator with a drag-and-drop canvas, WebSocket streaming, pluggable scheduling policies, and Gym-style environments for reinforcement-learning experiments.
The Workflow Gambit
Controlled nine-person experiment comparing AI-assisted development workflows by having each one build a UCI chess engine from the same specification and scoring the results with a shared evaluation harness.
Tech Stack
The tools I use to evaluate models, build agentic systems, and ship reliable data products.
Languages
AI / Machine Learning
Backend & Data
Cloud & Infrastructure
Let's build something
that learns.
I'm open to opportunities in AI/ML and software engineering. Drop me a line — I'd love to hear what you're working on.