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# Machine Learning Engineer

- **Company:** Poesis LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $200,000.0 - $280,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Python (Programming Language), Machine Learning, Tensorflow, Standard Sql, Pytorch, Large Language Models, Information Technology, Automation Anywhere
- **Published:** June 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=730bde97c5c20d59

## About the Role

Do you have experience in Startup experience?, Do you have a Bachelor's degree?, * 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)

## Description

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., * 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


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