Poesis AI

Engineering Manager

• Help define the architecture and technical direction of the Poesis platform
• Design and build scalable systems for financial data ingestion, processing, storage and access
• Build the infrastructure that supports research, model development, real-time multiagent inference, and production decision-making
• Design point-in-time data architectures that ensure reproducibility, historical accuracy and research integrity
• Define platform standards for data architecture, APIs, reliability, reproducibility and explainability
• Partner closely with researchers, quantitative developers and leadership to translate research into production.
• Define and evolve engineering processes that allow the team to ship quickly, reliably and predictably
• Recruit, develop and manage a high-performing engineering team

Head of Engineering

Full-stack Engineer

• Provide technical leadership across the agent platform
• Design and build agent-driven workflows and internal systems
• Build eval, observability, and feedback systems that improve agent reliability, performance, and learning over time
• Establish patterns and infrastructure for reliable agent deployment

Engineering Lead, Agents

Software Engineer

• Rapidly implement and iterate on research ideas and model prototypes.
• Clean, process, and join financial and fundamental datasets from professional and public sources.
• Build and maintain processes for feature generation, back-testing, and model evaluation.
• Run experiments, summarize results, and report findings to leadership.
• Contribute to code quality: testing, documentation, and integration into shared systems.
• Support the team in defining data schemas, APIs, and reproducibility standards.
• Implement, test, and refine models, signals, and analytical workflows.
• Maintain a consistent cadence of deliverables, focusing on iteration speed and reliability.

Quantitative Developer

Machine Learning Engineer

• 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