Machine Learning Developer

Diamondback Energy; Dallas (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Diamondback Energy is seeking an experienced MLOps Engineer to establish and evolve the ML delivery stack in a Dallas-area setting. You will collaborate with data scientists and engineers to productionize models using Databricks MLflow, AutoML, Unity Catalog, and robust CI/CD pipelines.

The role emphasizes scalable architecture, data quality, and governance across teams to ensure reliable, observable ML outcomes in production environments.

Qualifications

• Bachelor’s Degree in CS, Data Science, Engineering, Math, or related field.
• Hands-on Databricks MLflow and AutoML experience.
• 3–5 years building ML or data-intensive production systems.
• Strong Python proficiency with production-grade code.
• Strong SQL and Spark or equivalent distributed processing.
• Experience with MLOps: deployment, monitoring, lifecycle management.
• Git, unit testing, CI/CD, and design patterns.
• Ability to explain ML algorithms and tuning practices.
• Strong communication across teams.

Responsibilities

• Establish MLOps standards and golden path for production.
• Collaborate with data science teams to productionize models.
• Design and maintain automated CI/CD for training, deployment and promotion.
• Govern model lifecycle with experiment tracking, registration, versioning.
• Establish model and data monitoring and incident response.
• Ensure data quality and versioning for trusted inputs.
• Author docs, architectures, and playbooks; lead code reviews.
• Coordinate with stakeholders to drive adoption of shared frameworks.
• Evaluate new tools including LLM-assisted workflows and improvements.

Skills

Databricks MLflow

AutoML

Python

SQL

Spark

CI/CD

Git

ML Model Deployment

Experiment Tracking

Data Quality

Communication

Education

Bachelor’s Degree

Master’s Degree

Tools

Unity Catalog

Databricks Certification

Cloud Platforms

Containerization

Orchestration

Job description

Include but are not limited to

Job Responsibilities

• 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

Required Qualifications

• 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 hyper‑parameter tuning best practices
• Strong interpersonal, analytical, and communication skills, with the ability to work effectively across data science, engineering, and business teams

Preferred Qualifications

• 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.