Ty Clay

hey everyone! i'm samuel - 2nd year cs @uwaterloo - prev eng @memories_ai, ai research @uwaterloo - 99th percentile in multiple national math/coding contests - prev national level fencer; 275lbs max bench - 1.2k+ stars on github projects - turned down swe offers @Gemini @openart_ai and @ yc startups this summer to build smth of my own - got flown out for yc s26 interview yesterday; rejected - staying in sf for a few more days; looking to raise from other investors hmu if interested!

Jane Street hired a junior for $220K-$600K/year because he uses AI to analyze trillions of data points. In this 1-hour lecture, he shows exactly how he does it. Free. From the guy Wall Street is paying half a million to. You've been using AI to write captions. He's using it to print money on trillion-row datasets. Bookmark this instead of Netflix tonight. It pays for the rest of your career. Follow @codewithimanshu for more high-signal AI content from the people actually building the future. ↓ What he actually does for that paycheck. He builds machine learning systems for a trillion trillion floating point operations. Not "uses AI tools." Builds them. From scratch. At scale most engineers never touch in a full career. He's on the PyTorch core team. The same PyTorch that powers Jane Street, OpenAI, Anthropic, and every serious AI shop on earth. That's why the salary is $220K-$600K and not flat. Subpar year: $220K. Outstanding results: $600K+. Performance-based. Real impact. Real numbers. Wall Street isn't paying for credentials anymore. They're paying for engineers who can move trillion-row datasets through ML systems faster than anyone else on the planet. Follow @codewithimanshu for more breakdowns of the AI roles paying $500K+ in 2026. ↓ What this lecture actually teaches. This is not "AI for beginners." This is the exact technical foundation that turns a junior into the top-end of Jane Street's pay band: > How to architect ML pipelines for trillion-scale datasets > Why PyTorch internals matter at production scale > The optimization tricks that turn 10-hour jobs into 10-minute ones > Memory layouts and GPU kernels that hedge funds quietly weaponize > The mental models behind systems that move billions in trades This is Jane Street's edge being explained in public. Most engineers will watch 5 minutes, get scared, and click away. The ones who push through become the next $500K hires. Follow @codewithimanshu for breakdowns of every must-watch AI lecture worth your weekend. ↓ Why this matters more than any bootcamp. A 12-week ML bootcamp: $10,000-$15,000. A masters in ML at Stanford or CMU: $80,000+. This 1-hour lecture from a Jane Street insider: free. You've spent more on Uber Eats this month than this lecture costs. The gap between engineers earning $120K and engineers earning $500K+ isn't talent. It's exposure to content like this. People who watch it tonight understand AI infrastructure at the level Wall Street pays for. People who skip it stay competing with millions of other "AI engineers" using the same ChatGPT prompts. Same field. Different bank account. Save the video. Watch it tonight. Become the kind of engineer Jane Street fights other firms to hire. Follow @codewithimanshu for more high-signal AI content from the people actually building the future.