Propr.xyz is building a new Operating System for prop firms, helping them leverage blockchain technology to make them more efficient. We enable prop firms to leverage perpetual futures on Hyperliquid, prediction markets, and spot assets. We are actively deploying our technologies to the largest prop firms in the world. The pace is intense, but the journey is exciting. We only hire A-players. You need to be exceptional.

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 less than 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 less than 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.

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