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The Machine Learning (ML) Developer is the first dedicated ML Development role in the department and is responsible for establishing the development practices, standards, and platform foundations that move machine learning models from experimentation into reliable, governed production.

Working primarily within the Databricks ecosystem, the ML Developer will define how models are built, tracked, deployed, and monitored, and will coordinate with data science teams and technical professionals across the organization to ensure company objectives and goals are met.

• Establish the department’s MLOps standards, reusable pipeline patterns, and “golden path” for taking a model from notebook to production
• Partner with data science teams to productionize models using Databricks MLflow, AutoML, Unity Catalog, and Model Serving
• Design and maintain automated CI/CD pipelines for model training, deployment, and controlled promotion across environments
• Govern the model lifecycle through experiment tracking, model registration, versioning, lineage, and access control
• Establish model and data monitoring, validation checks, and operational observability; support incident response and reliability of production ML systems
• Enforce data and feature quality, schema validation, and data versioning so models train and infer on trusted inputs
• Author documentation, reference architectures, and playbooks; lead code reviews and knowledge-sharing to drive consistent engineering practice
• Coordinate with business stakeholders, data scientists, data engineers, and IT to define requirements and drive adoption of shared frameworks
• Evaluate emerging tools and patterns, including agentic and LLM-assisted development workflows, and recommend improvements to ML delivery

• Bachelor’s Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field
• Must have hands‑on experience with Databricks MLflow and AutoML
• Three (3) to five (5) years of hands‑on experience building, deploying, and operating machine learning or data‑intensive systems in production
• Strong proficiency in Python as a primary engineering language, with experience writing tested, maintainable production code
• Strong SQL skills and working knowledge of Spark or other distributed data processing frameworks
• Practical experience establishing or operating an MLOps workflow, including model deployment, pipeline automation, monitoring, and lifecycle management
• Software engineering fundamentals including version control (Git), unit testing, CI/CD, and common design patterns
• Ability to explain the intuition behind common ML algorithms and follow model training, evaluation, and hyperparameter tuning best practices
• Strong interpersonal, analytical, and communication skills, with the ability to work effectively across data science, engineering, and business teams

• Experience with Unity Catalog for model governance, lineage, and controlled promotion of ML assets
• Databricks certification (e.g., Databricks Certified Machine Learning Associate or Professional)
• Master’s Degree in a related field
• Familiarity with cloud data platforms, infrastructure‑as‑code, containerization and orchestration
• Exposure to LLM/GenAI application patterns such as RAG and evaluation harnesses, and to agentic or AI‑assisted development workflows
• Experience mentoring or training data scientists on engineering best practices
• Ability to operate both independently and as part of a team
• Self‑starter requiring minimal supervision with strong organizational and time management skills

Diamondback is an Equal Employment Opportunity Employer. Diamondback provides equal employment opportunities to all qualified applicants without regard to race, sex, sexual orientation, gender identity, national origin, color, age, religion, veteran or disability status, genetic information, pregnancy, or any other status protected by law.

Diamondback participates in E-Verify. Learn more about E-Verify.

Diamondback Energy is an independent oil and natural gas company headquartered in Midland, Texas focused on the acquisition, development, exploration, and exploitation of unconventional, onshore oil and natural gas reserves in the Permian Basin in West Texas.