Hypernova is building a distributed trading desk: an on‑chain prop trading protocol that gives independent traders the capital, tooling, and infrastructure usually reserved for institutions.
We find, fund, and enable the best traders. They prove an edge in an evaluation, get up to $200K initial funding across 110+ perpetual markets, and keep up to 80% of the profits, settled by smart contract in seconds. Rules, payouts, and reserves are anchored on‑chain for anyone to verify, and our terminal, analytics, and risk engine are built in‑house on Hyperliquid.
We're a team of seven, pre‑seed ($3M raised), one month into closed alpha with around 300 traders onboarded and close to $100K already paid out. The team has built trading and custody infrastructure at Coinbase and Citi, commodity‑derivatives pricing and risk systems at J.P. Morgan, and scaled platforms at Amazon. Our CEO previously led DeFi investing at RockawayX, including an early position in BreakoutProp (acquired by Kraken).
The Role
We're hiring a Founding Quant Researcher to build and lead the research behind our funding decisions. You'll work within the trading team, alongside the CTO and CRO, developing the classification, scaling, and hedging models that determine which traders to back, how far to scale them, and how the firm's exposure is managed as the book grows. This is a founding‑level mandate: you'll set the standard now and hire the researchers behind you as we scale.
What You'll Own
* Trader classification and scaling: the framework that separates durable edge from variance and governs how capital is allocated across traders.
* Hedging engine: the models and systems that manage balance‑sheet exposure across every supported instrument, executed on‑chain.
* Risk parameters and governance: evaluation parameters, risk limits, and the process that moves a parameter from proposal to production safely.
* Live risk surface: real‑time views of firm‑wide exposure, trader P&L, and the early‑warning signals the desk acts on.
* Stress modelling: forward‑looking models for volatility shocks, liquidity crunches, and correlation breaks, integrated into how decisions are made.
The Problems
The core of the work is statistical inference under pressure: judging skill from a short, noisy track record (across hundreds of simultaneous traders, accounting for multiple testing, survivorship, and shifting regimes) accurately enough to allocate capital against it. Around that sit the allocation decision of which flow to internalise versus hedge, the real‑time netting of thousands of correlated positions, and parameter design that holds up against traders actively probing it.
What We're Looking For
* Strong quantitative fundamentals across probability, statistics, and stochastic processes, applied to live trading and risk systems.
* Deep risk and portfolio knowledge: hedging, Greeks, VaR/CVaR, stress testing, exposure management.
* Working knowledge of trading systems: order types, matching and execution, mark/index pricing, funding, and liquidation mechanics, including their failure modes.
* Fluency with streaming market data (ticks, OHLC, orderbook) and the building of robust analytical pipelines.
* A track record of taking research to production with appropriate validation, monitoring, and guardrails.
* Strong Python (pandas, numpy, scipy), comfortable alongside a Go backend or able to ramp quickly.
* The ownership and judgement to lead a function and build a team behind you.
Nice to Have
* Crypto/DeFi-native, with familiarity with on‑chain execution constraints.
* Background in prop trading, market‑making, or exchange and clearing risk.
* Experience as an early or founding quant hire.
Compensation and Benefits
* Salary: $200,000 to $250,000 USD, depending on experience.
* Equity: meaningful founding‑level participation in Hypernova's ESOP.
* In‑office meals: fresh lunch and dinner served daily.
* Hardware: laptop and peripherals of your choice.
#J-18808-Ljbffr