Poesis
Member of Technical Staff | Quantitative Finance
Menlo Park, Remote. Posted a month ago
The role
You will work with the CEO, Chief Scientist, and AI and engineering teams on quantitative systems. You will prepare financial datasets, design and implement models and agents, and build tools for testing and evaluation.
What you will do
- Turn financial research and model prototypes into robust production systems
- Make complex financial data reliable for modeling and investment research
- Build scalable, reproducible systems for feature generation, backtesting and model evaluation
- Stress-test models and signals
- Help solve engineering challenges of moving research from experiment to production
Required qualifications
- Built infrastructure, data pipelines, or analytical tools for developing and evaluating financial models or trading strategies
- Enjoy working with complex linguistic and numerical data and producing rigorous, reproducible analysis
- Strong quantitative foundations and ability to reason about statistical and modeling problems
- Balance iteration speed with engineering quality
- Can explain technical findings and experimental results to leadership and technical and nontechnical audiences
- BS, MS, or PhD in Computer Science, Mathematics, Statistics, Physics, Finance, or a related quantitative field
Nice to have
- Full-time experience in finance, data science, or machine learning engineering
- Experience integrating professional financial data from Bloomberg, CapIQ, FactSet, or Refinitiv into quantitative research workflows
- Familiarity with portfolio optimization, risk modeling, or financial time series
Benefits
- Medical coverage
- Dental coverage
- Vision coverage
- Catered lunches
- Commuter benefits
- Relocation allowance
Before you apply
- Current legal authorization to work in the US required
- Continuing work visa sponsorship available for full-time employees
About the company
Poesis is an AI-native investment firm running autonomous agents that predict markets, construct portfolios, and manage risk. The founders managed institutional capital at Capital Group and led enterprise ML at Goldman Sachs and Amazon.