About CatPi
CatPi was incubated by a leading quantitative hedge fund with a 14-year history. We develop training datasets, research tasks, evaluation benchmarks, and reinforcement learning environments for financial AI.
The Role
Turn real investment and trading research into tasks for training and evaluating AI models. Work with quantitative researchers and engineers, and present findings and demonstrations to customers and partners.
Responsibilities
- Design research tasks covering signals, portfolio construction, backtesting, execution, risk, and attribution.
- Build reproducible Python workflows and expert-reviewed reference solutions.
- Develop automated checks and scoring criteria; identify errors in AI-generated research and code.
- Help adapt proprietary research tools and realistic market simulations into reinforcement learning environments.
- Present research findings, demonstrate sample tasks, and participate in customer discussions.
Qualifications
- Strong quantitative research experience in industry or academia.
- Proficiency in Python, statistics, financial data analysis, and backtesting.
- Sound understanding of data leakage, transaction costs, overfitting, and validation.
- Excellent communication and presentation skills.
- A quantitative graduate degree or equivalent practical experience.
Experience with hedge funds, trading strategies, market simulation, LLM evaluation, or reinforcement learning is a plus.
Compensation
Annual base salary of $150,000–$300,000, depending on experience and qualifications, plus a year-end performance-based bonus of $0–$150,000.
Bonuses depend on individual performance and are not guaranteed. Equity participation is negotiable.
Apply
Submit your resume and a brief description of a relevant research project. Public code or research examples are welcome. Please do not share confidential or proprietary materials.