Quantitative Researcher

About the roleMost job posts are designed to collect as many applications as possible.This is not one of them.At Simplify, we work directly with startup founders to help them hire for high-priority roles.We’re partnering with an AI research lab and trading firm in Palo Alto to hire Founding Members of Technical Staff — exceptional early-career engineers and quantitative developers who want to build the systems, signals, and infrastructure behind a new trading firm. $200–350K base, and they’re hiring multiple engineers now.About the companyOne-liner: Building intelligence systems for live markets — and deploying them first through its own quantitative hedge fundStage: $29M seed round led by Ribbit Capital, with backing from Lux Capital, Valor Equity Partners, the founder of DRW, the CEO of Kalshi, and founders of DoorDash and Ramp. Small founding team, in person in Palo Alto.The team includes former xAI and Citadel talent, IMO medalists, and highly ranked crypto and Polymarket traders. Its research lab develops AI systems for market prediction, initially deployed through an eight-figure internal hedge fund anchored by the founder of IMC Trading.This is for researchers who understand what production-grade alpha research actually looks like—but would rather help build the firm than remain one researcher inside a large, mature organization.What you'll work onDesign, research, and test systematic trading strategies end to endBuild and improve the signals and models that drive live tradingDevelop the research and backtesting infrastructure needed to evaluate ideas rigorouslyWork with market, alternative, and real-time datasetsTake research from hypothesis through validation and into productionWork directly with engineers and AI researchers to ship research quicklyMonitor live performance and improve strategies as market conditions changeHelp establish the research culture, methodology, and standards of an early teamWhat we look for2+ years of quantitative research experience at a top-tier firm such as HRT, Jane Street, Citadel Securities, or a comparable organizationA track record of owning research that made it into productionExperience developing systematic signals or trading strategies—not only supporting research infrastructureStrong statistical fundamentals and sound experimental judgmentStrong programming ability in Python and/or C++Able to explain what failed, why it failed, and how you controlled for overfitting, leakage, costs, and changing market regimesDeep, personal interest in markets—you trade, follow them closely, and think about them outside your assigned workIn person in Palo Alto, 5 days/weekNice to haveComing off garden leave or reaching the end of a noncompetePreviously left quantitative trading for a startup and now want to return to marketsEnd-to-end ownership of a strategy from data and hypothesis through production and monitoringExperience with alternative data, prediction markets, or AI/ML-driven researchExperience building research infrastructure in addition to using itInterest in shaping the standards and culture of a new research organizationWhy joinFund economics upsideResearch has a direct path into an existing eight-figure trading vehicleOwn strategies end to end instead of contributing one isolated piece inside a large podHelp define the firm’s research process, technical standards, and cultureWork directly with a founding team spanning frontier AI, quantitative trading, and prediction marketsBuild new research infrastructure without inheriting years of organizational constraintsMeaningful influence over what the firm researches, how it evaluates ideas, and how quickly it shipsH-1B sponsorship available