Machine Learning Developer
Diamondback Energy13 days ago
Dallas, TX, USAMid Level / Senior
Responsibilities
- Establish MLOps standards, reusable pipeline patterns, and production workflows for moving machine learning models from notebooks to production.
- Partner with data science teams to productionize models using Databricks MLflow, AutoML, Unity Catalog, and Model Serving.
- Design and maintain CI/CD pipelines for model training, deployment, and controlled promotion across environments.
- Govern model experiment tracking, registration, versioning, lineage, access control, monitoring, validation, and operational observability.
- Enforce data and feature quality, schema validation, and data versioning for model training and inference.
- Support incident response and reliability of production machine learning systems.
- Create documentation, reference architectures, and playbooks; lead code reviews and knowledge-sharing.
- 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.
Requirements
- Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related field.
- Three to five years of hands-on experience building, deploying, and operating machine learning or data-intensive systems in production.
- Hands-on experience with Databricks MLflow and AutoML.
- Strong proficiency in Python and 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 MLOps workflows, including model deployment, pipeline automation, monitoring, and lifecycle management.
- Software engineering fundamentals including Git, unit testing, CI/CD, and common design patterns.
- Ability to explain common machine learning algorithms and apply model training, evaluation, and hyperparameter-tuning best practices.
- Strong interpersonal, analytical, and communication skills for working across data science, engineering, and business teams.
- Preferred qualifications include Unity Catalog experience, Databricks certification, a related master’s degree, cloud data platform experience, infrastructure-as-code, containerization and orchestration familiarity, LLM/GenAI application patterns such as RAG and evaluation harnesses, and experience mentoring data scientists.