Our Thesis Markets are not random walks. They are high-dimensional physics problems. For decades, quantitative finance has been dominated by "Quant 1.0":…
Our Thesis Markets are not random walks. They are high-dimensional physics problems. For decades, quantitative finance has been dominated by "Quant 1.0": linear regressions, statistical arbitrage, and mean reversion. These methods assume markets are stationary and noise is normally distributed. In a hyper-connected world, these assumptions are collapsing. We believe we are entering the "AI Native" era of finance. Just as LLMs learned the underlying structure of language, our Foundation Models for Finance (FMFs) are learning the latent physics of price action. By treating the entire market history not as a spreadsheet, but as a sequential language problem, we can identify non-linear causal chains that are invisible to traditional statistical methods. We don't just fit curves. We build agents that understand market microstructure. Neural Execution Execution policies optimize for fill quality and impact under microstructure constraints, learning when to provide or take liquidity. Self-Optimizing Infrastructure Elastic compute that shifts capacity between training and inference based on volatility and market regimes. A Institutional grade environment for validating strategies under market microstructure constraints that is built to reduce the gap between backtest and live deployment. We are a small team of researchers and engineers solving the hardest problems in finance. We are currently hiring for the following roles. You will focus on feature engineering and alpha generation.
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