Machine Learning Engineer

About Poesis

Asset management is the largest industry not yet rebuilt around AIPoesis is leading that future. We are an AI-native investment manager building a system of self-improving agents to predict market movements and outperform legacy managers. Were building systems that discover alpha manage risk and compound intelligence over time led by founders who spent their careers managing institutional capital and building enterprise-level AI. This is frontier research with immediate real-world validation where your work directly impacts investment decisions and portfolio performance. Were a new breed of investment firm and were looking for world-class talent to shape the path forward together.

About the Role

Were hiring an ML Engineer who will turn research and data into production models. Youll build ML pipelines end-to-end from ingesting and cleaning data to model training validation and signal generation. This is a deeply hands-on execution-oriented role for someone who can write code design experiments and deliver validated results quickly. Youll work directly with the Poesis leadership team owning both implementation and iteration. Over time youll help scale the system into a full production platform and define best practices for future hires.

Responsibilities

• Architect build and maintain the core ML infrastructure for Poesis investment

• 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.

• Integrate with professional financial data providers (e.g. Bloomberg CapIQ).

• Refine foundational MLOps practices: model versioning CI/CD workflow orchestration monitoring and reproducible deployment.

• Define and iterate on demo-able workflows that connect model outputs to investment decision-makers.

Required Competencies

• 510 years of experience as an ML Engineer Quant Developer or similar role.

• Prior experience in an agentic startup frontier AI lab a leading hedge fund a big tech platform team or similar

• Proven track record deploying production ML systems (ideally in finance or other high-stakes domains).

• Deep expertise in Python and ML frameworks; required: scikit-learn and XGBoost; preferred: PyTorch or TensorFlow.

• 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 large-scale or time-sensitive data handling.

Preferred Competencies

• 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.

• Experience working with Claude Code Codex or other coding agents.

• Experience with LLM/RAG workflows for parsing financial documents (filings transcripts).

• Experience deploying workflow pipelines on AWS

Location

Hybrid: 3 days per week on-site at our office in Menlo Park CA. Relocation allowance available.

Benefits

We offer excellent medical dental and vision coverage alongside a strong benefits package that includes catered lunches in our Menlo Park office commuter benefits and more.

Current legal authorization to work in the US required; continuing work visa sponsorship available for full-time employees.

Working at Poesis

As an early team member youll help shape not just the product but how the company operates. Your decisions will have lasting impact across the business. Youll build from first principles with no legacy systems or entrenched processes slowing you down. Our team is made up of people from elite companies and universities who are low ego collaborative and excited to build together.

Required Experience:

IC