Poesis-posted 2 months ago
Full-time • Senior
San Francisco, CA

Poesis is building an ML-driven hedge fund focused on daily trading decisions. We’re hiring our Founding Head of Engineering, the first engineering hire who will define our architecture, build the initial system, and act as both IC and product owner. You’ll work directly with the CEO, CFO, and Chief Scientist to create the technical backbone of our fund. You’ll design and ship the first demo-able workflows, integrate with financial data providers, build reproducible pipelines, and create a thin UI layer that makes outputs usable for decision-makers and investors. Over time, you’ll help recruit engineers and data scientists as the company grows.

  • Own the architecture and development of Poesis’ first engineering systems: data ingestion, model/optimizer orchestration, and productization.
  • Act as product manager: define workflows, release criteria, and usability standards in collaboration with leadership.
  • Build reproducible pipelines and lightweight interfaces (CLI, dashboards, GUIs) for internal users and investor demos.
  • Integrate various professional financial data provider (e.g., CapIQ, Bloomberg, FactSet, Refinitiv).
  • Establish team practices: sprint cadence, release process, versioning, and documentation.
  • Mentor and coordinate future hires.
  • 6–10+ years of software engineering experience, including senior/staff IC roles.
  • Experience building data-intensive or quant systems from scratch.
  • Strong systems design and API/interface definition skills.
  • Fluency in Python and modern data tooling (pandas/numpy, SQL, orchestration frameworks).
  • Track record of productizing research or analytics into usable tools (dashboards, GUIs, or internal products).
  • Comfortable building robust systems that can execute real world trades with extremely high accuracy.
  • Comfortable working directly with executives and acting as de-facto PM.
  • Willingness to work in-person in the Bay Area; relocation support available.
  • Prior experience with professional financial data providers (CapIQ, Bloomberg, FactSet, Refinitiv).
  • Familiarity with quantamental investing, portfolio optimization, or financial ML.
  • Exposure to ML ops practices (feature stores, model registries, evaluation frameworks).
  • Basic experience with LLM/RAG tooling for document processing (filings, transcripts).
  • Basic understanding of trading and financial markets.
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