Machine Learning Engineer

A high-potential, early-stage FinTech and algorithmic trading tech company engineering next-generation quantitative execution and systematic trading platforms for institutional investors, hedge funds, and proprietary trading desks.

Backed by multi-million-dollar venture funding with proven market traction and active paying enterprise clients, the company operates with an agile, high-caliber team of quantitative researchers and systems engineers.

The company delivers a proprietary platform seamlessly integrating advanced quantitative research with ultra-low-latency execution engines to deploy systematic algorithmic trading strategies.

Located in the Central District with convenient transit access (a short walk to train transit and adjacent to the light rail), working in-office.

Role Description-

• Serving as an Applied Machine Learning / Quantitative Researcher, reporting directly to the CTO, driving advanced algorithmic discovery and production-grade ML implementations for systematic trading.
• Formulating, developing, and backtesting novel Machine Learning and statistical modeling methodologies to solve complex predictive challenges across high-frequency and dynamic market environments.
• Designing and executing rigorous quantitative experiments, backtests, and statistical validations on high-dimensional, noisy time-series financial datasets.
• Building end-to-end research pipelines in Python, turning abstract mathematical concepts and exploratory signals into robust, deployable algorithms.
• Analyzing market microstructure, non-stationary signals, and feature representations to maximize model predictive power and minimize execution slippage.
• Collaborating closely with quantitative developers and systems architects to ensure seamless translation of algorithmic strategies into live, ultra-low-latency trading environments.

Requirements

• 2 years of experience as a Machine Learning Researcher or Applied Researcher – Mandatory
• M.Sc. or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, Physics, or related quantitative field from a leading university – Mandatory
• Strong, hands-on research expertise working with Time-Series data, noisy data, or high-dimensional real-world datasets – Mandatory
• Advanced programming and research implementation skills in Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow) – Mandatory
• Deep theoretical grounding in statistical modeling, hypothesis testing, and rigorous experiment validation – Mandatory
• Highly analytical mindset, intellectual curiosity, and fluent communication in English (written and verbal) – Mandatory

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