1 day ago
Menlo Park, CA, USASenior
Base Salary
$180k - $280k/yr
Responsibilities
- Prepare large financial datasets for quantitative modeling and investment research.
- Design and implement quantitative models and agents.
- Build reliable, scalable, and reproducible systems for feature generation, backtesting, and model evaluation.
- Convert financial research and model prototypes into robust production systems.
- Stress-test models and signals to assess their performance and weaknesses.
- Explain technical findings and experimental results to technical and nontechnical audiences.
Requirements
- Bachelor’s, master’s, or doctoral degree in computer science, mathematics, statistics, physics, finance, or a related quantitative field.
- Experience building infrastructure, data pipelines, or analytical tools for financial models or trading strategies.
- Strong quantitative foundations and the ability to reason about statistical and modeling problems.
- Ability to balance rapid iteration with engineering quality and production robustness.
- Preferred: full-time experience in finance, data science, or machine learning engineering.
- Preferred: experience integrating Bloomberg, CapIQ, FactSet, or Refinitiv data into quantitative research workflows.
- Preferred: familiarity with portfolio optimization, risk modeling, or financial time series.
Benefits
- Hybrid work arrangement with three days per week onsite at the Menlo Park, California office.
- Relocation allowance available.
- Medical, dental, and vision coverage.
- Catered lunches and commuter benefits.
- Continuing work visa sponsorship available for full-time employees.
Categories
Data EngineeringML Engineering
About Poesis
Whoever builds the leading intelligence for finance will create far more than returns. Poesis is the AI-native asset manager running autonomous agents that predict markets, construct portfolios, and manage risk. Live capital is the training ground for an intelligence that compounds with every signal.