Quant Developer

Shah Equity is building a strategy engine designed to ship 36+ quant strategies per year across crypto and US equities. We need quantitative developers who have actually shipped systems to production not built academic backtests. The team you're joining: a PM with active trading book, a Head of AI Engineering building the agent infrastructure, a Quant Validation Engineer whose job is to break your code, and the founder who personally cares about every strategy that goes live.

Mission

Build, test, and deploy production trading strategies. Partner with traders to algorithmize their edge. Pair with the validation engineer on stress-testing. Ship code that runs unattended with real money.

90-Day Outcomes

• Ship two production strategies by Day 60
• Build or extend the backtesting framework (we already have a baseline)
• Establish documentation and code review practices the team adopts
• Successfully convert at least one manual trader's strategy into an algorithm

What You Bring

• 2+ years of production quantitative development experience
• Strong Python (mandatory). C++ or Rust a plus.
• Backtesting framework experience you've built one or extensively used one
• Comfort with execution platforms IB API, Alpaca, Polygon, Binance API, or equivalent
• Demonstrable strategy you have personally built and tested (not just modified from a tutorial)
• Statistics fluency you can talk through Sharpe, Sortino, drawdown, walk-forward, and overfitting prevention

How You'll Use AI

• Daily use of Claude or Cursor for code generation and review
• AI-assisted backtest analysis and edge case identification
• LLM-assisted documentation — every strategy you ship has full written documentation
• Partnership with Head of AI Engineering on the agent layer that orchestrates strategy execution

Compensation

4.5-11 LPA based on profile depth. Performance bonus tied to strategies you ship to production AND their actual P&L. Top performers may receive equity at the operating-entity level on strategies they own end-to-end.

Work Sample (After Initial Screen)

72 hours: build a small backtest of a simple mean-reversion strategy on data we provide. Submit GitHub repository with: code, backtest results, written analysis of the strategy's weaknesses, and your approach to preventing overfitting. We grade on code quality, statistical rigor, and the honesty of your weakness analysis (if you say the strategy is perfect, we know you didn't look hard enough).

Application Instructions (READ CAREFULLY)

Apply by emailing [email protected].

Subject line MUST be exactly: [QUANTDEV-2026] Your Full Name — Primary Language

Example: [QUANTDEV-2026] Priya Sharma — Python

In the email body, include exactly three things, numbered:

• 1. One GitHub or code repository link (must be public or accessible)
• 2. One strategy you've personally built, in one sentence
• 3. The most complex bug you debugged in a backtest, in two sentences

Applications that follow these instructions are processed first. The instruction is itself a filter — quant developers who can't follow precise written specs aren't quant developers we want.