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# Staff Machine Learning Engineer

- **Company:** Pivotal Solutions Inc
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Batch Processing, Data as a Services, Data Mining, Python (Programming Language), Machine Learning, Software Engineering, Feature Engineering, Flask (Web Framework), Fastapi, Pandas, Scikit Learn, Information Technology, Machine Learning Operations
- **Published:** November 4, 2026
- **Apply:** https://www.careerbuilder.com/job-details/staff-machine-learning-engineer-san-francisco-ca--67134c57-8ea2-4b74-a8ae-112dd1c073dc

## About the Role

Machine Learning, Loans, Process Improvement, Marketing Strategy, Machine Learning, Equipment Maintenance/Repair, Product Support, Mentoring, Data Science, Best Practices, * PhD or Master's degree in Computer Science, Statistics, Engineering, or a related field.

 * 5+ years of experience as a Machine Learning Engineer, Data Scientist, or in a closely related role, with a proven track record of delivering impactful solutions.
 * Deep expertise in machine learning algorithms, frameworks, and the full ML lifecycle, including data extraction, feature engineering, model serving, and monitoring for both live and batch processing.
 * Strong software design skills with high proficiency in Python and related libraries/frameworks (e.g., Scikit-Learn, Pandas, Flask, FastAPI).
 * Excellent interpersonal skills, with strong written and verbal communication abilities.
 * Demonstrated experience with cloud providers (AWS preferred) and associated data services.

## Description

* Collaborate with global teams to deliver high-impact data products for worldwide deployment.
 * Develop and maintain ML pipelines to optimize critical processes for global lending products, including anti-fraud systems, credit strategy, and marketing optimization.
 * Enhance and maintain scalable machine learning infrastructure to support robust data product performance.
 * Mentor engineers and data scientists on best practices in ML engineering, fostering a culture of excellence and knowledge sharing.
 * Lead data product development from ideation through to large-scale deployment, ensuring end-to-end execution.