{"schemaVersion":"jobsearcher.job.v1","id":"7ea00974bb84394ef17997c0","url":"https://jobsearcher.com/jobs/7ea00974bb84394ef17997c0","canonicalUrl":"https://jobsearcher.com/jobs/7ea00974bb84394ef17997c0","title":"Machine Learning Developer","description":"CURRENT EMPLOYEES - Please apply using \"Jobs Hub\" in Workday. This career site is for external applicants only.\n\nThe 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.\n\nJob Responsibilities:\n\nInclude but are not limited to\n\nEstablish the department’s MLOps standards, reusable pipeline patterns, and “golden path” for taking a model from notebook to production\nPartner with data science teams to productionize models using Databricks MLflow, AutoML, Unity Catalog, and Model Serving\nDesign and maintain automated CI/CD pipelines for model training, deployment, and controlled promotion across environments\nGovern the model lifecycle through experiment tracking, model registration, versioning, lineage, and access control\nEstablish model and data monitoring, validation checks, and operational observability; support incident response and reliability of production ML systems\nEnforce data and feature quality, schema validation, and data versioning so models train and infer on trusted inputs\nAuthor documentation, reference architectures, and playbooks; lead code reviews and knowledge-sharing to drive consistent engineering practice\nCoordinate with business stakeholders, data scientists, data engineers, and IT to define requirements and drive adoption of shared frameworks\nEvaluate emerging tools and patterns, including agentic and LLM-assisted development workflows, and recommend improvements to ML delivery\n\nRequired Qualifications:\n\nBachelor’s Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field\nMust have hands-on experience with Databricks MLflow and AutoML\nThree (3) to five (5) years of hands-on experience building, deploying, and operating machine learning or data-intensive systems in production\nStrong proficiency in Python as a primary engineering language, with experience writing tested, maintainable production code\nStrong SQL skills and working knowledge of Spark or other distributed data processing frameworks\nPractical experience establishing or operating an MLOps workflow, including model deployment, pipeline automation, monitoring, and lifecycle management\nSoftware engineering fundamentals including version control (Git), unit testing, CI/CD, and common design patterns\nAbility to explain the intuition behind common ML algorithms and follow model training, evaluation, and hyperparameter tuning best practices\nStrong interpersonal, analytical, and communication skills, with the ability to work effectively across data science, engineering, and business teams\n\nPreferred Qualifications:\n\nExperience with Unity Catalog for model governance, lineage, and controlled promotion of ML assets\nDatabricks certification (e.g., Databricks Certified Machine Learning Associate or Professional)\nMaster’s Degree in a related field\nFamiliarity with cloud data platforms, infrastructure-as-code, containerization and orchestration\nExposure to LLM/GenAI application patterns such as RAG and evaluation harnesses, and to agentic or AI-assisted development workflows\nExperience mentoring or training data scientists on engineering best practices\nAbility to operate both independently and as part of a team\nSelf-starter requiring minimal supervision with strong organizational and time management skills\n\nDiamondback 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 .","company":"Diamondback Energy","rawCompany":"diamondback energy","city":"Dallas","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-08-28T10:18:31.051Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Developer","description":"CURRENT EMPLOYEES - Please apply using \"Jobs Hub\" in Workday. This career site is for external applicants only.\n\nThe 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.\n\nJob Responsibilities:\n\nInclude but are not limited to\n\nEstablish the department’s MLOps standards, reusable pipeline patterns, and “golden path” for taking a model from notebook to production\nPartner with data science teams to productionize models using Databricks MLflow, AutoML, Unity Catalog, and Model Serving\nDesign and maintain automated CI/CD pipelines for model training, deployment, and controlled promotion across environments\nGovern the model lifecycle through experiment tracking, model registration, versioning, lineage, and access control\nEstablish model and data monitoring, validation checks, and operational observability; support incident response and reliability of production ML systems\nEnforce data and feature quality, schema validation, and data versioning so models train and infer on trusted inputs\nAuthor documentation, reference architectures, and playbooks; lead code reviews and knowledge-sharing to drive consistent engineering practice\nCoordinate with business stakeholders, data scientists, data engineers, and IT to define requirements and drive adoption of shared frameworks\nEvaluate emerging tools and patterns, including agentic and LLM-assisted development workflows, and recommend improvements to ML delivery\n\nRequired Qualifications:\n\nBachelor’s Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field\nMust have hands-on experience with Databricks MLflow and AutoML\nThree (3) to five (5) years of hands-on experience building, deploying, and operating machine learning or data-intensive systems in production\nStrong proficiency in Python as a primary engineering language, with experience writing tested, maintainable production code\nStrong SQL skills and working knowledge of Spark or other distributed data processing frameworks\nPractical experience establishing or operating an MLOps workflow, including model deployment, pipeline automation, monitoring, and lifecycle management\nSoftware engineering fundamentals including version control (Git), unit testing, CI/CD, and common design patterns\nAbility to explain the intuition behind common ML algorithms and follow model training, evaluation, and hyperparameter tuning best practices\nStrong interpersonal, analytical, and communication skills, with the ability to work effectively across data science, engineering, and business teams\n\nPreferred Qualifications:\n\nExperience with Unity Catalog for model governance, lineage, and controlled promotion of ML assets\nDatabricks certification (e.g., Databricks Certified Machine Learning Associate or Professional)\nMaster’s Degree in a related field\nFamiliarity with cloud data platforms, infrastructure-as-code, containerization and orchestration\nExposure to LLM/GenAI application patterns such as RAG and evaluation harnesses, and to agentic or AI-assisted development workflows\nExperience mentoring or training data scientists on engineering best practices\nAbility to operate both independently and as part of a team\nSelf-starter requiring minimal supervision with strong organizational and time management skills\n\nDiamondback 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 .","datePosted":"2026-08-28T10:18:31.051Z","dateModified":"2026-08-28T10:18:31.051Z","hiringOrganization":{"@type":"Organization","name":"Diamondback Energy","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Dallas","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"7ea00974bb84394ef17997c0"},"url":"https://jobsearcher.com/jobs/7ea00974bb84394ef17997c0"}}