About Poesis
Poesis is the AI-native investment manager pioneering a new foundation model for investing in US equities and other asset classes. We're building modular AI systems to predict market movements and outperform legacy managers. This is frontier research with immediate real-world validation. Your work will directly shape investment decisions and portfolio performance.
Poesis is located near Stanford University and we are looking for full-time local candidates to work hybrid in office and remote.
About The Role
Poesis is building an ML-driven hedge fund focused on ML driven trading. We’re hiring a Founding Quant Engineer to help turn research ideas into production-grade code. You’ll work alongside the Head of Engineering and Chief Scientist to build data pipelines, implement models, and ensure results are clean, reproducible, and explainable.
This is a hands-on, high-learning-curve role ideal for someone with strong technical fundamentals who wants exposure to both engineering and quantitative finance in a startup setting. This is a highly execution-oriented role: you’ll receive strong direction from Poesis’ Chief Scientist and CEO and be responsible for turning their research ideas and specifications into tested, production-ready code.
Location: San Francisco Bay Area (in-office several days per week).
Responsibilities
- Rapidly implement and iterate on research ideas and model prototypes.
- Clean, process, and join financial and fundamental datasets from both professional and public sources.
- Build and maintain scripts for feature generation, back-testing, and model evaluation.
- Run experiments, summarize quantitative results, and report findings to leadership.
- Contribute to code quality: testing, documentation, and integration into shared systems.
- Support the Head of Engineering in defining data schemas, APIs, and reproducibility standards.
- Directly support the Chief Scientist (CSO) and Chief Executive Officer (CEO) by implementing, testing, and refining models, signals, and analytical workflows that inform daily trading decisions.
- Maintain a consistent cadence of deliverables—focusing on iteration speed and reliability.
Required Competencies
- BS or MS in Computer Science, Mathematics, Statistics, Physics, Finance or related quantitative field.
- Strong Python skills (pandas, numpy, scipy, matplotlib); comfort with SQL.
- Experience working with real-world datasets and building reproducible analyses or pipelines.
- Basic understanding of statistics, regression, optimization, and ML fundamentals.
- Clear communicator who can explain technical findings to non-specialists.
- Willingness to work in-person in the Bay Area and collaborate closely with a small founding team.
- Professional experience in financial data science.
Preferred Competencies
- Prior internship or project experience in finance, data science, or ML engineering.
- Familiarity with APIs from Bloomberg, CapIQ, FactSet, or Refinitiv.
- Exposure to portfolio optimization, risk modeling, or financial time-series.
- Experience with git, Docker, and modern orchestration tools (Prefect, Airflow, etc.).
- Early-stage startup experience or demonstrated builder mindset.
Profile
- You’re early in your career but serious about mastering both data engineering and quantitative modeling.
- You want to see your code directly influence trading and investment decisions.
- You thrive in a small, fast-moving environment with direct mentorship and high ownership.
- You care about correctness, clarity, and learning the “why” behind financial data.
Current legal authorization to work in the US required; visa sponsorship considered later for full-time employees.
