Staff Machine Learning Engineer
Pivotal Solutions
Salary undisclosed
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Description
Responsibilities
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.
Requirements
Qualifications
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.
About this role
Pivotal Solutions is looking for a Staff Machine Learning Engineer to own the full lifecycle of machine learning systems that power their global lending products—from anti-fraud detection to credit strategy and marketing optimization. You'd work across distributed teams to build and maintain ML pipelines and infrastructure at scale, while also mentoring other engineers and data scientists on ML best practices.
This role requires deep technical depth: a Master's or PhD in a quantitative field, at least five years of hands-on ML engineering or data science experience, and strong command of Python, ML frameworks, and the complete pipeline from data extraction through model serving and monitoring. You should be comfortable with cloud infrastructure (AWS especially) and able to design robust systems for both real-time and batch processing. Beyond the technical bar, you'll need solid communication skills to collaborate effectively across teams and guide others.
The position is based in San Francisco and requires in-office work. It's a full-time role suited to someone ready to step into a senior technical position with both individual impact and leadership responsibilities.
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Pay for this role
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