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 an exceptional Machine Learning Engineer to help build the systems that make this possible. In this role, you'll develop models, signals and evaluation frameworks that power investment decision-making across the platform. You'll work across the full machine learning lifecycle, from experimentation and model and agent development to deployment and iteration, with significant ownership over both research and production outcomes. This is a unique opportunity to help build a new approach to investing powered by AI.

Responsibilities

Rapidly implement and iterate on machine learning models, signals and research ideas
Design and run experiments to evaluate and improve model and agent performance and investment impact
Build reproducible workflows for feature generation, training, validation and evaluation
Work with large-scale financial, fundamental and alternative datasets to identify predictive signals and improve model performance

Required Competencies

5+ years experience as a Machine Learning Engineer, or related role
Prior experience at a frontier AI lab, agentic startup, leading hedge fund, big tech company, or similar
Strong Python and SQL skills, with experience working with large-scale datasets
Experience developing, evaluating and deploying machine learning models in production environments
Success building reproducible research workflows and experimentation frameworks
Familiarity with modern AI systems, including LLMs, evaluation frameworks, and agent workflows
Skill leveraging Claude Code, Codex, or other coding agents
BS/MS/PhD in Computer Science or a related field, or equivalent practical experience

Preferred Competencies

Experience developing ML and AI systems using financial, fundamental, alternative, or time-series datasets
Familiarity with quantitative investing, portfolio construction, or risk management
Experience with PyTorch or TensorFlow, and AI workflows for parsing financial documents (filings, transcripts)

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, 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.

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