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Member of Technical Staff Quantitative

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Member of Technical Staff Quantitative in USA new

  • Menlo Park, CA

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.

Salary: $180k - $280k/yr

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 sig...

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