Quantitative Developer

About Poesis

Asset management is the largest industry not yet rebuilt around AI—Poesis is leading that future. We are an AI-native investment manager building a system of self-improving agents to predict market movements and outperform legacy managers. We’re building systems that discover alpha, manage risk, and compound intelligence over time, led by founders who spent their careers managing institutional capital and building enterprise-level AI. This is frontier research with immediate real-world validation where your work directly impacts investment decisions and portfolio performance. We're a new breed of investment firm, and we're looking for world-class talent to shape the path forward together.

About the Role

We’re hiring a Quant Engineer 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 will work alongside Poesis’ Chief Scientist, CEO, and engineering leadership to turn research ideas and specifications into tested, production-ready code.

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 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

• 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.

• Understanding of statistics, regression, optimization, and ML fundamentals.

• Clear communicator who can explain technical findings to non-specialists.

• Professional experience in financial data science.

Preferred Competencies

• Prior 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.).

• Experience working with Claude Code, Codex, or other coding agents.

• 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

We offer excellent medical, dental, and vision coverage, alongside 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.

Working at Poesis

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.