The job, in the employer's words
About Poesis
Whoever builds the leading intelligence for finance will create far more than returns. Poesis is the AI-native investment firm running autonomous agents that predict markets, construct portfolios, and manage risk. Our founders managed institutional capital at Capital Group ($3T AUM) and led enterprise ML at Goldman Sachs and Amazon. We're building a new type of firm, where live capital is the training ground for an intelligence that compounds with every signal.
About the Role
At Poesis, machine learning and artificial intelligence open the door to improved alpha discovery, higher quality decision-making and intelligent risk management. We’re looking for a Member of Technical Staff (Machine Learning) to build the models, signals, and evaluation tools that make this possible. This is an opportunity to test new ideas against complex, real-world financial data and see the impact of your work on actual investment decisions.
Challenges You'll Work On
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.
About You
You’ve worked at a frontier AI lab, agentic startup, high-growth technology company, leading investment manager, or similar organization.
You’ve developed, evaluated, and deployed ML models in production.
You understand how models fail, how evaluation design shapes outcomes and how to distinguish promising results from noise.
You have strong quantitative judgment and experience working with large, complex datasets.
You 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).
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, 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, you’ll help shape not just the product, but how the company operates. Your decisions will have lasting impact across the business. You’ll 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.
Benefits mentioned
- We offer excellent medical, dental, and vision coverage, alongside a strong benefits package that includes catered lunches, commuter benefits, and more. Current legal authorization to work in the US required; continuing work visa sponsorship available for full-time employees.
Requirements mentioned
- You’ve worked at a frontier AI lab, agentic startup, high-growth technology company, leading investment manager, or similar organization.
- You’ve developed, evaluated, and deployed ML models in production.
- You understand how models fail, how evaluation design shapes outcomes and how to distinguish promising results from noise.
- You have strong quantitative judgment and experience working with large, complex datasets.
- You 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.
- 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).
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