Responsibilities

• Support and enhance the real-time risk engine processing 10k+ position updates/second across perpetuals, spots, and prediction markets.
• Design and implement risk metrics: portfolio VaR, stress VaR, expected shortfall, Greeks aggregation, cross-asset correlations.
• Build position limit frameworks: notional caps, delta limits, concentration limits, leverage constraints, drawdown thresholds.
• Develop statistical models for tail-risk scenarios: fat-tailed distributions, regime switching, correlation breakdowns.
• Implement margin calculation engines: cross-margining logic, liquidation price models, maintenance margin monitoring.
• Work closely with trading infrastructure team to ensure <50ms P99 latency for risk calculations on critical paths.
• Create real-time dashboards and alerting systems: exposure heatmaps, PnL attribution, limit breaches, anomaly detection.
• Backtest risk models against historical liquidation events and high-volatility periods to validate accuracy.
• Design circuit breakers and kill switches for extreme market conditions or system anomalies.

Requirements

• 3+ years of experience in quantitative risk, trading systems, or financial engineering.
• Strong foundation in statistics, probability theory, and risk modeling (VaR, CVaR, ES, stress testing).
• Proficiency in Python with NumPy, Pandas, SciPy for quantitative analysis and backtesting.
• Experience with real-time risk systems processing 1000+ updates/second with <50ms latency.
• Deep understanding of derivatives pricing: perpetual funding rates, mark-to-market, liquidation mechanics.
• Portfolio risk metrics: Greeks (delta, gamma, vega), correlation matrices, beta hedging, tail risk.
• Experience with crypto perpetuals (funding rates, cross-margining, liquidation cascades).
• Familiarity with prediction markets (AMM mechanics, Kelly criterion, order book dynamics).
• Time-series analysis: volatility modeling (GARCH, EWMA), regime detection, autocorrelation.
• SQL proficiency for risk aggregation queries across millions of position updates.
• Ability to translate complex risk concepts into real-time monitoring systems.
• Understanding of margin calculations, position sizing, and drawdown controls.

Bonus

• Experience with Hyperliquid API (WebSocket feeds, vault risk monitoring, liquidation engine).
• Background in prop trading, market making, or hedge fund risk management (2-sigma+ shops preferred).
• Knowledge of blockchain-specific risks: oracle failures, MEV, liquidation cascades, network congestion.
• Proficiency with TypeScript, Node.js, NestJS for building production risk services.
• Experience with event-driven architectures, message queues (Redis Streams, Kafka), CQRS patterns.
• Time-series databases (TimescaleDB, InfluxDB) for storing tick-level risk snapshots.
• Machine learning for anomaly detection: isolation forests, autoencoders, change point detection.
• Understanding of regulatory frameworks (CFTC, SEC, MiFID II) and compliance monitoring.
• Experience with Monte Carlo simulations, copula models, or extreme value theory.
• Published research or contributions to quantitative finance / risk management literature.
• DevOps: Docker, AWS (ECS, Aurora), Terraform, monitoring tools (Grafana, Datadog).

How to apply

We ask candidates to submit their application via a POST request to our API. This helps us identify candidates who read job descriptions carefully and have basic technical skills.

POST

https://propr.xyz/api/hiring/apply

Request body:

{ "roleSlug": "quant-risk", "name": "Your Name", "email": "[email protected]", "link": "https://linkedin.com/in/yourprofile", "coverNote": "Why Propr?", "exceptionalNote": "What makes you exceptional?", "telegramHandle": "@yourhandle", "appUid": "optional-trading-terminal-uid"
}