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Founding Machine Learning Engineer at Poesis | San Francisco

PoesisSan Francisco
Hybrid Full-time

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Experience Level

Entry Level

Qualifications

Required Competencies:You should possess strong programming skills in Python and have hands-on experience with machine learning frameworks such as TensorFlow or PyTorch. A solid understanding of statistical modeling and data analysis is essential, along with familiarity with MLOps practices. Excellent problem-solving abilities and a proactive mindset are crucial for success in this role.

About the job

About Poesis
Poesis is a pioneering AI-native investment manager that is transforming the landscape of U. S. equities through innovative foundation models. We are developing cutting-edge AI systems designed to predict market trends and exceed the performance of traditional investment managers. This exciting work represents frontier research that is validated in real-world scenarios. Your contributions will play a crucial role in influencing investment strategies and enhancing portfolio outcomes.

Location & Workstyle
Located in the vibrant San Francisco Bay Area, near Stanford, we support a Hybrid work model, requiring several days on-site each week.

Relocation assistance is available.

About the Role

As a Founding Machine Learning Engineer, you will be the first full-time ML hire at Poesis, responsible for translating research and data into scalable production models. You will develop the initial ML pipelines from the ground up, managing everything from data ingestion and preprocessing to model training, validation, and signal generation. This role is ideal for a hands-on professional who excels in coding, designing experiments, and quickly delivering validated results.

You will collaborate closely with the CEO and Chief Scientist, taking ownership of both the implementation process and iterative improvements. As the system scales, you will help transition it into a full production platform and establish best practices for future team members.

Responsibilities

  • Design, develop, and maintain the foundational ML infrastructure for Poesis’ investment platform.

  • Create reproducible pipelines for data ingestion, feature engineering, and model training.

  • Establish backtesting and evaluation frameworks with defined performance metrics.

  • Provide regular, detailed reports on model accuracy, feature significance, and overall portfolio impact.

  • Work closely with the Chief Scientist to refine model hypotheses and assess production readiness.

  • Ensure high code quality through version control, testing, reproducibility, and thorough documentation.

  • Develop robust backtesting frameworks and model validation tools, incorporating walk-forward evaluation and risk management controls.

  • Integrate with leading financial data providers such as Bloomberg, FactSet, Refinitiv, and CapIQ.

  • Implement foundational MLOps practices, including model versioning, CI/CD, monitoring, and documentation.

  • Define and refine “demo-able” workflows that link model outputs to investment decision-makers.

About Poesis

Poesis is at the forefront of AI-driven investment management, revolutionizing how investment decisions are made through advanced machine learning technologies. We are committed to fostering a dynamic work environment where innovation thrives, and our team members are empowered to make impactful contributions.

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