Member of Technical Staff | Quantitative Finance

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.

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.

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  Rapidly implement and iterate on research ideas and model prototypes.

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  Clean, process, and join financial and fundamental datasets from professional and public sources.

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  Build and maintain processes for feature generation, back-testing, and model evaluation.

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  Run experiments, summarize results, and report findings to leadership.

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  Contribute to code quality: testing, documentation, and integration into shared systems.

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  Support the team in defining data schemas, APIs, and reproducibility standards.

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  Implement, test, and refine models, signals, and analytical workflows.

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  Maintain a consistent cadence of deliverables, focusing on iteration speed and reliability.

• 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.

• 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.

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

As an early team member, you’ll help shape not just the product, but how the company operates. Your decisions will have lasting impact across the business. You’ll build from first principles, with no legacy systems, or entrenched processes slowing you down. Our team is made up of people from elite companies and universities who are low ego, collaborative, and excited to build together.