Member of Technical Staff
US$180,000 – US$280,000 / yr base salary
Posted 13 Oct 2026
Advertised as “Member of Technical Staff | Quantitative Finance”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
- Visa sponsorship
- Offered
| Base salary | US$180,000 – US$280,000 / yr |
|---|
Field of studyComputer Science, Finance, Mathematics, Physics, Statistics (or related)
Study levelBachelor's or higher
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 looking for an exceptional Member of Technical Staff (Quantitative Finance) to work alongside our CEO, Chief Scientist, and AI and engineering teams on the quantitative systems behind our investment process. You’ll prepare large financial datasets, design and implement models and agents, and build tools for testing and evaluation. This is an opportunity to work close to the research itself, moving new ideas from experiment to production, and seeing your work inform investment decisions.
Challenges You'll Work On
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Turn financial research and model prototypes into robust production systems.
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Make complex financial data reliable enough for rigorous modeling and investment research.
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Build scalable, reproducible systems for feature generation, backtesting and model evaluation.
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Stress-test models and signals to understand what holds up and what breaks down.
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Help solve the engineering challenges of moving research quickly from experiment to production.
About You
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You’ve built infrastructure, data pipelines, or analytical tools for developing and evaluating financial models or trading strategies.
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You enjoy working with complex linguistic and numerical data and producing rigorous, reproducible analysis.
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You have strong quantitative foundations and can reason clearly about statistical and modeling problems.
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You balance iteration speed with engineering quality and know when a prototype needs to be made more robust.
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You can explain technical findings and experimental results to leadership and technical and nontechnical audiences.
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You have a BS, MS, or PhD in Computer Science, Mathematics, Statistics, Physics, Finance, or a related quantitative field.
Nice to Have
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Full-time experience in finance, data science, or machine learning engineering.