Founding Quant Developer

7 days ago

San Francisco, California, United States Poesis Full time
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.

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