Machine Learning Engineer

Poesis-posted 2 months ago
San Francisco, CA
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Poesis is building an AI-driven hedge fund focused on daily trading decisions, not high-frequency trading. We’re hiring our Founding ML Engineer, the first full-time machine learning hire who will turn research and data into production models. You’ll build the first ML pipelines end-to-end — from ingesting and cleaning data, to model training, validation, and daily signal generation. This is a deeply hands-on, execution-oriented role for someone who can write code, design experiments, and deliver validated results quickly. You’ll work directly with the CEO, CFO, and Chief Scientist, owning both implementation and iteration. Over time, you’ll help scale the system into a full production platform and define best practices for future hires. Location: San Francisco Bay Area (in-office; relocation available)

  • Architect, build, and maintain the core ML infrastructure for Poesis’ investment platform.
  • Develop reproducible pipelines for data ingestion, feature generation, and model training.
  • Implement backtesting and evaluation frameworks with clear performance metrics.
  • Deliver regular, documented reports on model accuracy, feature importance, and portfolio-level impact.
  • Collaborate closely with the Chief Scientist to refine model hypotheses and production readiness.
  • Maintain code quality: version control, testing, reproducibility, and documentation.
  • Build robust backtesting frameworks and model validation tools with walk-forward evaluation and risk controls.
  • Integrate with professional financial data providers (Bloomberg, FactSet, Refinitiv, CapIQ).
  • Establish foundational MLOps practices: model versioning, CI/CD, monitoring, and documentation.
  • Define and iterate on 'demo-able' workflows that connect model outputs to investment decision-makers.
  • 5–10+ years of experience as an ML Engineer, Quant Engineer, or similar role.
  • Proven track record deploying production ML systems (ideally in finance or other high-stakes domains).
  • Deep expertise in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn, JAX, XGBoost).
  • Experience designing large-scale, reliable data or MLOps systems.
  • Strong software engineering fundamentals: testing, versioning, CI/CD, and code review discipline.
  • Experience with financial data APIs and real-time data handling.
  • Comfortable working directly with executives and acting as both IC and product owner.
  • Willingness to work in-person in the Bay Area; relocation support available.
  • Prior experience at a hedge fund, quant research lab, or fintech startup.
  • Familiarity with quantitative finance, portfolio optimization, or risk management.
  • Exposure to time-series modeling, forecasting, or reinforcement learning.
  • Understanding of financial market microstructure and execution systems.
  • Experience with LLM/RAG workflows for parsing financial documents (filings, transcripts).
  • Comfort with multi-language engineering environments (C++, Rust, Go, etc.).
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