Machine Learning Engineer

An elite, highly specialized Quantitative Trading and algorithmic investment enterprise developing proprietary high-throughput trading platforms, autonomous market-making systems, and automated digital asset management strategies across global exchanges.

Founded and directed by renowned industry pioneers with distinguished executive track records across premier global algorithmic trading companies, the enterprise is backed by substantial multi-year institutional capital reserves ensuring absolute financial resilience.

The core R&D laboratory operates as a focused, elite quantitative research and engineering group tackling ultra-low latency, real-time distributed architectures, and high-frequency data modeling challenges.

The culture uniquely combines top-tier scientific and engineering rigor, direct business impact, and an agile, streamlined interview process.

Located directly adjacent to primary train transit, operating on an in-office model with one day of remote work per week.

Role Description-

• Serving as a Quantitative Researcher / Senior ML & Data Scientist, driving the research, mathematical modeling, and production deployment of predictive trading algorithms and automated market strategies.
• Developing and optimizing state-of-the-art Machine Learning and Deep Learning models for Time Series forecasting, volatility estimation, and signal generation across dynamic financial datasets.
• Conducting rigorous quantitative research on large-scale tick-level and order-book data, extracting actionable alpha signals from noisy real-world market environments.
• Designing end-to-end algorithmic experimentation workflows, including robust backtesting frameworks, statistical hypothesis testing, and risk attribution modeling.
• Collaborating closely with Quantitative Software Engineers to translate quantitative prototypes into deterministic, ultra-low latency execution engines.
• Continuously analyzing market anomalies, microstructure shifts, and execution telemetry to enhance strategy resilience and profitability.

Requirements-

• B.Sc. in an Exact Science discipline from an accredited research university with honors (GPA 90+ / Magna Cum Laude) – Mandatory (Highest priority for Mathematics, Statistics, or Physics)
• At least 5 years of proven professional hands-on experience in Machine Learning, Deep Learning, or Data Science – Mandatory
• Up-to-date hands-on experience (current or immediate prior role) developing and deploying models with Time Series data – Mandatory
• Strong mathematical foundations across statistical modeling, probability theory, stochastic calculus, or linear algebra – Mandatory
• Full fluency in Python and modern quantitative/ML stacks (e.g., PyTorch, TensorFlow, NumPy, Pandas, Scipy) – Mandatory
• Professional technical communication and documentation capabilities in English (written and verbal) – Mandatory

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