Production ML Engineer, Digital Experience (Onsite)

Pure Storage, Inc.; Santa Clara (CA); On-site; USD 180,000 - 270,000; Full time

Job summary

Everpure is seeking a Machine Learning Engineer for the Digital Experience Insights team in Santa Clara, CA. This role focuses on taking ML models from prototype to production, building end-to-end pipelines, and ensuring models run reliably at scale in a fast-moving analytics environment.

You will design data pipelines, feature stores, and workflow orchestration, while collaborating with data scientists and engineers to address changing business problems.

Qualifications

• Bachelor's, Master’s, or Ph.D. in Computer Science, Data Science, Engineering, Statistics, or related field, or equivalent practical experience.
• 3–5 years of industry experience in data engineering, ML engineering, or hybrid roles with a track record of shipping models to production.
• Strong software engineering fundamentals in Python and SQL, including production-quality, well-tested code.

Responsibilities

• Productionize models: build reliable, scalable pipelines for training, serving, and inference.
• Design and maintain data pipelines, feature stores, and workflow orchestration for clean inputs.
• Build monitoring for model performance, data drift, and pipeline health.
• Develop and validate ML models when needed, collaborating with data science to shape design.
• Collaborate cross-functionally with Data Scientists, Data Engineers, and Software Engineers to translate business questions into a production system.

Skills

Python

SQL

Production-grade code

Workflow orchestration

Education

Bachelor's/Master's/PhD in CS/DS/Engineering

Tools

Airflow

Dagster

PyTorch

Scikit-Learn

Job description

Everpure is seeking a Machine Learning Engineer for the Digital Experience Insights team in Santa Clara, CA. This role focuses on taking ML models from prototype to production, building end-to-end pipelines, and ensuring models run reliably at scale in a fast-moving analytics environment.

You will design data pipelines, feature stores, and workflow orchestration, while collaborating with data scientists and engineers to address changing business problems.