7 months ago
San Francisco, CA, USASenior / Staff+
Responsibilities
- 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.
Requirements
- 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, 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.
Benefits
- High quality dental, vision, and health care.