About Loaf

Australian luxury property has quietly outperformed equities for decades, but it’s always been locked away. $5M–$50M harbourfront homes in Vaucluse, Point Piper, and Mosman, reserved for global elites. You needed millions to get in. You waited months to get out. And there was no way to trade property the way you trade anything else.

Until now.

Loaf Markets is building the world’s first liquid property exchange — giving investors access to the country’s most exclusive real estate, backed 1:1 by land title, and tradeable in nanoseconds.

Traditional property takes 30–90 days to settle and months to sell. Loaf settles in one second.

Our pipeline sits at $300M+ in premium property. We’re backed by property developers, family offices, and property owners. Our team comes from Macquarie, TikTok, IMC, Nasdaq, Goldman Sachs, Citadel, and Microsoft.

loafmarkets.com

The Role

Your models will directly move capital on a live exchange. You’ll build the intelligence layer behind Loaf Liquidity — pricing models that value real estate in real time, trading signals that feed a market making engine, and anomaly detection that keeps our markets clean. The P&L impact of your work shows up the same day.

We sit at the intersection of property data, financial markets, and machine learning. If you want your work to have direct financial consequences.

What’s Involved

• Predictive pricing models — combining traditional property valuation with alternative data and ML techniques
• Trading signal generation — pipelines that feed directly into the market making system
• Automated valuation models (AVMs) — real-time fair value estimates for exchange-listed assets
• Anomaly detection — trading activity, market manipulation, pricing outliers
• Feature engineering — property data, macroeconomic indicators, on-chain activity, sentiment
• Research to production — work with the quant trader to integrate models into live execution

Required

• 3+ years building ML models in production — deployed systems with real-time inference, not just notebooks
• Strong Python (scikit-learn, PyTorch/TensorFlow, pandas) and data engineering experience (SQL, Spark, or similar)
• Experience with time series forecasting, regression, or anomaly detection
• Understanding of financial data and markets — you don’t need to be a trader, but you need to know what a spread is
• Comfortable building from scratch in an ambiguous environment

Preferred

• Experience in quantitative finance, algorithmic trading, or fintech
• Familiarity with property data (CoreLogic, Domain, ABS) or alternative data sources
• NLP for sentiment analysis on property and market news
• Bayesian methods or probabilistic programming