AI-Native Quant Algorithm Engineer

About the Role & Team Culture

• Flat Research Hierarchy: Minimal bureaucracy with direct collaboration alongside Strategy Partners and Portfolio Managers, ensuring rapid research-to-production cycles.
• Open Factor Ecosystem: Complete access to a globally shared factor library, operator infrastructure, and feature pipelines—no information silos or redundant effort.
• AI-Native Workflow: We actively leverage AI agents, LLMs, and intelligent development tools to augment research. We prioritize system architecture, mathematical rigor, and logic verification over manual boilerplate coding.

Key Responsibilities

• AI-Driven Alpha Discovery: Utilize AI workflows to rapidly translate top-tier academic literature into executable backtests (Paper-to-Code), mining high-capacity Alpha signals from Level-2/Tick, order book, and alternative datasets.
• Feature Engineering & Modeling: Train and evaluate advanced models (Transformers, GNNs, Time-Series Models, GBDT) with strict anti-overfitting protocols (cross-validation, signal denoising).
• Workflow Automation: Integrate LLMs and autonomous AI agents into daily research pipelines to automate hyperparameter tuning, factor generation, and real-time PnL attribution.
• Architecture & Code Auditing: Act as a quantitative code architect to review, optimize, and audit AI-generated code for execution efficiency, logical consistency, and look-ahead bias prevention.

Qualifications

• Education: Master’s or Ph.D. in Computer Science, Mathematics, Physics, Statistics, or Financial Engineering.
• AI-Native Competency:
• Expert command of AI development tools and prompt engineering to build agent workflows.
• Quantitative Foundation: Solid grounding in probability theory, linear algebra, time-series forecasting, and machine learning principles.
• Mindset: Highly self-driven, detail-oriented, and deeply committed to transparent, collaborative research.

Preferred Experience

• 0–3 years of Alpha research experience in quantitative funds or proprietary trading desks with a verifiable track record.
• Experience fine-tuning LLMs, building RAG architectures, or designing RL/AI Agents for financial applications.
• ACM/ICPC awards, Kaggle top placements, or publications in top AI conferences (NeurIPS, ICML, KDD).

Pay: $30,000.00 - $50,000.00 per month

Benefits:

• Meal provided
• Medical Insurance
• Opportunities for promotion
• Professional development

Work Location: In person