About Poesis

Asset management is the largest industry not yet rebuilt around AI—Poesis 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. We’re 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. We're a new breed of investment firm, and we're looking for world-class talent to shape the path forward together.

About the Role

We’re hiring an ML Engineer who will turn research and data into production models. You’ll 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. You’ll work directly with the Poesis leadership team, owning both implementation and iteration. Over time, you’ll 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 platform.

• 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

• 5–10+ 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, 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.