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Our client is the AI-native investment manager pioneering a new foundation model for investing in U.S. equities. We're building modular AI systems to predict market movements and outperform legacy managers. This is frontier research with immediate real-world validation. Your work will directly shape investment decisions and portfolio performance. Their building an ML-driven hedge fund focused on reshaping how trading decisions are made. 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
Job Responsibility:
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 providers (e.g., CapIQ, Bloomberg, FactSet, Refinitiv)
Establish team practices: sprint cadence, release process, versioning, and documentation
Mentor and coordinate future hires
Requirements:
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
Current legal authorization to work in the US required
Nice to have:
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