Machine Learning Engineer, Digital Experience

Pure Storage•Santa Clara, CA

1d•Onsite

About The Position

As a Machine Learning Engineer on the Digital Experience Insights team, you'll help take machine learning models from prototype to production, building the pipelines, infrastructure, and engineering practices that let models run reliably at scale. You'll also build and validate models yourself when needed, but the core of the role is making sure good models actually make it into production and stay healthy once they're there. This role sits within a fast-moving analytics organization where the specific projects shift over time, so we're looking for someone who can adapt their approach to whatever problem is in front of them.

Requirements

Bachelor's, Master's, or Ph.D. in Computer Science, Data Science, Engineering, Statistics, or a related field, or equivalent practical experience.
3-5 years of industry experience in data engineering, ML engineering, or a hybrid data science/engineering role, with a track record of shipping models to production.
Strong software engineering fundamentals in Python and SQL, including writing production-quality, well-tested code.
Hands-on experience with a workflow orchestration tool, such as Airflow or Dagster.
Experience working in a cloud-native environment (AWS, GCP, or Azure).
Working knowledge of machine learning and statistical modeling, with familiarity with a library such as Scikit-Learn or PyTorch, and enough grounding to build or extend a model when needed.
Good communication skills, with the ability to explain technical work clearly to non-technical stakeholders.
Comfort working through ambiguity and shifting priorities, and a collaborative approach to working across teams.

Responsibilities

Productionization: Take models from prototype to production, building reliable, scalable pipelines for training, serving, and inference.
Data & ML Infrastructure: Design and maintain data pipelines, feature stores, and workflow orchestration so models have clean, timely, well-tested inputs.
Monitoring & Reliability: Build monitoring for model performance, data drift, and pipeline health, and respond when something breaks.
Model Development: Build and validate machine learning models and statistical approaches when needed, working closely with the broader data science team on model design.
Cross-Functional Collaboration: Work with Data Scientists, Data Engineers, and Software Engineers to turn open-ended business questions into a clear technical plan and a working production system.