Quantitative Developer

About Poesis

Whoever builds the leading intelligence for finance will create far more than returns. Poesis is the AI-native investment firm running autonomous agents that predict markets, construct portfolios, and manage risk. Our founders managed institutional capital at Capital Group ($3T AUM) and led enterprise ML at Goldman Sachs and Amazon. We're building a new type of firm, where live capital is the training ground for an intelligence that compounds with every signal.

About The Role

We’re hiring a Quantitative Developer to help turn research ideas into production-grade code. You’ll help build data pipelines, implement models and ensure results are clean, reproducible and explainable. You’ll work alongside Poesis’ Chief Scientist, CEO and engineering leadership to turn large-scale data and quantitative research into models, signals and tools that drive investment decision-making.

Responsibilities

• Rapidly implement and iterate on research ideas and model prototypes.
• Clean, process, and join financial and fundamental datasets from professional and public sources.
• Build and maintain processes for feature generation, back-testing, and model evaluation.
• Run experiments, summarize results, and report findings to leadership.
• Contribute to code quality: testing, documentation, and integration into shared systems.
• Support the team in defining data schemas, APIs, and reproducibility standards.
• Implement, test, and refine models, signals, and analytical workflows.
• Maintain a consistent cadence of deliverables, focusing on iteration speed and reliability.

Required Competencies

• 3+ years of professional experience building the model infrastructure, data pipelines, and analytical tools to drive trading strategies
• Strong Python skills (pandas, numpy, scipy, matplotlib); comfort with SQL
• Skill working with Claude Code, Codex, or other coding agents
• Proficiency working with real-world financial datasets and building reproducible analyses or pipelines
• Understanding of statistics, regression, optimization, and ML fundamentals
• Clear communicator who can explain technical findings to non-specialists
• BS/MS/PhD in Computer Science, Mathematics, Statistics, Physics, Finance or related quantitative field

Preferred Competencies

• Prior full-time 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
• Skill with git, Docker, and modern orchestration tools (Prefect, Airflow, etc.)
• Early-stage startup experience or demonstrated builder mindset

Location

Hybrid: 3 days per week on-site at our office in Menlo Park, CA. Relocation allowance available.

Benefits

• Adequate medical, dental, and vision coverage
• A strong benefits package that includes catered lunches in our Menlo Park office, commuter benefits, and more
• Current legal authorization to work in the US required; continuing work visa sponsorship available for full-time employees

Compensation Range

$180K - $280K