Founding Quant Developer

About the Role

Poesis is building an ML-driven hedge fund focused on daily-frequency (not high-frequency) 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 Investment Officer (CIO) 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.