Machine Learning Developer

Machine Learning DeveloperThe 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.Job Responsibilities:Include but are not limited to:Establish the department's MLOps standards, reusable pipeline patterns, and "golden path" for taking a model from notebook to productionPartner with data science teams to productionize models using Databricks MLflow, AutoML, Unity Catalog, and Model ServingDesign and maintain automated CI/CD pipelines for model training, deployment, and controlled promotion across environmentsGovern the model lifecycle through experiment tracking, model registration, versioning, lineage, and access controlEstablish model and data monitoring, validation checks, and operational observability; support incident response and reliability of production ML systemsEnforce data and feature quality, schema validation, and data versioning so models train and infer on trusted inputsAuthor documentation, reference architectures, and playbooks; lead code reviews and knowledge-sharing to drive consistent engineering practiceCoordinate with business stakeholders, data scientists, data engineers, and IT to define requirements and drive adoption of shared frameworksEvaluate emerging tools and patterns, including agentic and LLM-assisted development workflows, and recommend improvements to ML deliveryRequired Qualifications:Bachelor's Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related fieldMust have hands-on experience with Databricks MLflow and AutoMLThree (3) to five (5) years of hands-on experience building, deploying, and operating machine learning or data-intensive systems in productionStrong proficiency in Python as a primary engineering language, with experience writing tested, maintainable production codeStrong SQL skills and working knowledge of Spark or other distributed data processing frameworksPractical experience establishing or operating an MLOps workflow, including model deployment, pipeline automation, monitoring, and lifecycle managementSoftware engineering fundamentals including version control (Git), unit testing, CI/CD, and common design patternsAbility to explain the intuition behind common ML algorithms and follow model training, evaluation, and hyperparameter tuning best practicesStrong interpersonal, analytical, and communication skills, with the ability to work effectively across data science, engineering, and business teamsPreferred Qualifications:Experience with Unity Catalog for model governance, lineage, and controlled promotion of ML assetsDatabricks certification (e.g., Databricks Certified Machine Learning Associate or Professional)Master's Degree in a related fieldFamiliarity with cloud data platforms, infrastructure-as-code, containerization and orchestrationExposure to LLM/GenAI application patterns such as RAG and evaluation harnesses, and to agentic or AI-assisted development workflowsExperience mentoring or training data scientists on engineering best practicesAbility to operate both independently and as part of a teamSelf-starter requiring minimal supervision with strong organizational and time management skillsDiamondback 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.