Databricks Machine Learning (ML) Administrator

We are seeking an experienced Databricks Machine Learning (ML) Administrator to own the end\u2011to\u2011end administration, governance, and secure operations of our ML environments on Databricks. In this role, you will configure and manage ML compute, enforce access and governance for MLflow assets (experiments and model registry), and ensure reliable model training, deployment, and serving at scale. You will partner closely with Data Engineering, ML Engineering, Security, and FinOps to deliver a robust, compliant, and cost\u2011efficient ML platform.\n\nKey Responsibilities\n\nPlatform Operations \u0026 Compute\n\n * Deploy, configure, and maintain Databricks ML clusters (CPU/GPU), SQL Warehouses, and cluster policies optimized for ML workloads; apply autoscaling, pools, and runtime selection (including Databricks Runtime for ML).\n * Administer Jobs and Pipelines that orchestrate training, evaluation, and batch/real\u2011time scoring; manage run\u2011as identities and default privileges to meet least\u2011privilege requirements.\n * Establish and enforce compute access controls (attach/restart/manage) and workspace object permissions; standardize policies to prevent configuration drift.\n\n\n\nML Lifecycle Governance (MLflow \u0026 Serving)\n\n * Govern MLflow Experiments and Registered Models with fine\u2011grained permissions (read/edit/manage), standardizing experiment tracking, model versioning, stage transitions, and approvals.\n * Operate and secure model serving endpoints, including permissions for view, query, and manage actions; implement change control for deployments.\n\n\n\nData Access \u0026 Unity Catalog Alignment\n\n * Coordinate with data governance to implement metastore, catalog, schema, and table\u2011level permissions that support feature engineering, training, and evaluation while safeguarding sensitive data.\n * Apply enterprise identity and access management patterns across account and workspace scopes (users, groups, service principals) using SCIM/SSO standards.\n\n\n\nSecurity, Compliance \u0026 Auditability\n\n * Enforce workspace object ACLs, compute isolation modes, secret handling, and log\u2011access controls for ML clusters; implement Spark ACL settings per policy.\n * Operationalize system tables/audit logs and usage analytics to meet regulatory and internal control requirements; partner with Security/GRC for periodic reviews.\n\n\n\nReliability, Monitoring \u0026 Incident Response\n\n * Monitor cluster health, job success/failure, serving endpoint SLOs, and capacity; establish alerting and incident runbooks for ML infrastructure.\n * Lead post\u2011incident reviews and continuous improvement for platform reliability and developer productivity.\n\n\n\nCost Management \u0026 FinOps\n\n * Implement and iterate compute policies, budget policies, and usage dashboards to optimize GPU/CPU consumption for ML training and serving.\n\n\n\nEnablement \u0026 Best Practices\n\n * Define and evangelize ML platform standards: environment baselines, cluster policies, experiment hygiene, model promotion flows, and serving change\u2011management.\n * Partner with ML teams to align platform features (AutoML, Feature/Vector stores, model serving) to use cases and performance targets.\n\n\n\nRequired Qualifications\n\n * 5+ years administering Databricks or similar ML/data platforms (e.g., Spark\u2011based platforms) with hands\u2011on experience in workspace administration, compute policies, and MLflow governance.\n * Proven expertise managing Databricks permissions (workspaces, clusters, jobs, experiments, registered models, serving endpoints) via UI, REST/CLI.\n * Strong understanding of Unity Catalog concepts and implementing catalog/schema/table access for ML workflows.\n * Working knowledge of Python/Scala sufficient to understand notebooks, init scripts, and operational tooling (no application development required).\n * Experience with SSO/SCIM, enterprise identity providers, and group\u2011based access patterns across account and workspace scopes.\n * Familiarity with audit logging, system tables, and cost\u2011management techniques in Databricks.\n\n\n\nPreferred Qualifications\n\n * Databricks Platform Administrator accreditation (or equivalent) and experience with serverless/SQL warehouses, cluster pools, and model serving.\n * Experience operationalizing run\u2011as service principals for jobs and pipelines and separating ownership vs. execution permissions.\n * Exposure to infrastructure\u2011as\u2011code (e.g., Terraform) for permissions/policies and environment baselining.\n * Understanding of data protection controls (masking, row/column access) and secure handling of secrets and logs in ML contexts.\n\n\n\nTools \u0026 Technologies You Will Use\n\n * Databricks Workspace \u0026 Account Console, Unity Catalog, Jobs, Pipelines, MLflow, Model Serving, Databricks Runtime for ML, SQL Warehouses.\n * Databricks CLI/REST APIs for permissions and automation; optional IaC (Terraform) for policy/permission as code.\n\n\n\n## Qualifications\n\n### Education:\n\nBachelor\u0027s Degree (Required)\n\n### Skills\n\nBig Data Platforms, Databricks Platform, Databricks SQL, Data Engineering, Machine Learning (ML)\n\n### Certifications:\n\n### Languages:\n\n### Years of Experience:\n\n7 - 10 Years\n\n### Work Experience:\n\n## Additional Information\n\n### \n\n### Shift:\n\nDay (Canada)\n\n### \n\n### Travel:\n\nYes, 20% of the Time\n\n### \n\n### Relocation Eligible:\n\nNo\n\n### Referral Payment Plan:\n\nEmployee Referral (Standard)\n\nApplied Materials is an Equal Opportunity Employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law. \n