Building at the intersection of ML systems,
quantitative finance, and AI infrastructure.
I'm a CS and mathematics student at NYU's Courant Institute, building production ML systems and studying quantitative methods. I've deployed LLM pipelines in production, designed hyperscale data center architectures, and written about AI infrastructure for industry audiences.
Selected for Point72 Academy's Spring Sessions, secured $150K in funding at Yotta 2025, and enabled NYU's access to 8/16-GPU AI training clusters on second-life Meta servers.
When I'm not in the terminal, I'm writing about cross-industry dynamics in technology, mining, and food systems.
Building across ML engineering, AI product, and quant finance research.
From low-latency C++ engines to GPU programming curricula.
High-performance limit orderbook engine with integrated ML signal layer — built in C++17 for microsecond-latency market simulation. The matching engine and ML inference share the same memory space.
A structured, hands-on GPU programming curriculum bridging hello world and advanced ML kernel optimization.
Enabled NYU access to 8/16-GPU AI training clusters on repurposed Meta server hardware. Designed the architecture and documentation for student researchers.
Click a region to see what's living rent-free in my brain.
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Essays on AI infrastructure, quant finance, and cross-industry dynamics.
Select an article to read.
Pitch decks and investment analyses across cleantech, biotech, and growth-stage SaaS.
Investment analysis of BoxPower's containerized solar+storage microgrid platform. Off-grid power for remote communities, critical infrastructure, and data centers — $10M revenue, $236B global market.
Investment thesis for AttoTude — the world's first THz Radio Over Wire for AI and hyperscale data center interconnects. 1.6 Tb/s chip-to-chip speeds on standard CMOS, targeting the $32B+ DCI market.
Sector investment thesis covering stem cells, CAR-T, and Natural Killer cells — market landscape, clinical pipeline, regulatory dynamics, and thesis for allogeneic off-the-shelf oncology platforms.
Open to quant research roles, ML engineering internships, and interesting conversations.
hello@jeanpark.dev