Member of Technical Staff | Machine Learning

The role

This person will build models, signals, and evaluation tools for an AI-native investment firm. You will test new ideas against complex, real-world financial data to impact investment decisions.

What you will do

• Find predictive signals in large financial, fundamental and alternative datasets.
• Design experiments and evaluations that separate real improvements from noise.
• Build rigorous, reproducible ML research systems for fast experimentation.
• Turn promising research into production models and signals that hold up over time.
• Use real-world outcomes to understand where models and agents can improve.

Required qualifications

• Worked at a frontier AI lab, agentic startup, high-growth technology company, leading investment manager, or similar organization.
• Developed, evaluated, and deployed ML models in production.
• Understand how models fail, how evaluation design shapes outcomes and how to distinguish promising results from noise.
• Strong quantitative judgment and experience working with large, complex datasets.
• Can take ownership of open-ended research problems and use evidence to decide what to pursue next.
• BS, MS, or PhD in Computer Science or a related field, or equivalent practical experience.

Nice to have

• Experience developing ML or AI systems using financial, fundamental, alternative, or time-series data.
• Familiarity with quantitative investing, portfolio construction, or risk management.
• Experience applying modern AI systems to financial documents (filings, transcripts, etc).

Benefits

• Medical coverage
• Dental coverage
• Vision coverage
• Catered lunches
• Commuter benefits

Before you apply

• Current legal authorization to work in the US required
• Continuing work visa sponsorship available for full-time employees

About the company

Poesis is an AI-native investment firm running autonomous agents that predict markets, construct portfolios, and manage risk. The founders managed institutional capital at Capital Group and led enterprise ML at Goldman Sachs and Amazon.