ML Platform Engineer: Production ML & MLOps (Databricks)

Diamondback Energy; Dallas (TX); On-site; USD 120,000 - 170,000; Full time

Job summary

Diamondback Energy seeks an ML Developer to establish development practices, standards, and platform foundations for moving models from experimentation to reliable production. The role focuses on the Databricks ecosystem, defining how models are built, tracked, deployed, and monitored in collaboration with data science teams across the organization.

Responsibilities include establishing MLOps standards, enabling production-ready pipelines, and governing model lifecycles with robust

Qualifications

• Must have hands-on experience with Databricks MLflow and AutoML.
• Three to five years of hands-on experience building, deploying, and operating ML or data-intensive systems in production.
• Strong proficiency in Python with tested, maintainable production code and strong SQL skills.

Responsibilities

• Establish ML development practices, standards, and platform foundations for production-grade models.
• Partner with data science teams to productionize models using Databricks tools.
• Design and maintain automated CI/CD pipelines for model training and deployment.
• Govern the model lifecycle with experiment tracking, versioning, and governance.
• Coordinate with stakeholders to define requirements and drive adoption of shared ML frameworks.
• Evaluate new tools and patterns and recommend improvements to ML delivery.

Skills

Python

SQL

Spark

Git & CI/CD

MLOps fundamentals

Team collaboration

Problem solving

Education

Bachelor's degree in CS/Engineering/Math/Statistics

Master's degree (preferred)

Tools

Databricks MLflow

AutoML

Unity Catalog

Model Serving

Git

Job description

Diamondback Energy seeks an ML Developer to establish development practices, standards, and platform foundations for moving models from experimentation to reliable production. The role focuses on the Databricks ecosystem, defining how models are built, tracked, deployed, and monitored in collaboration with data science teams across the organization.

Responsibilities include establishing MLOps standards, enabling production-ready pipelines, and governing model lifecycles with robust