Databricks Machine Learning (ML) Administrator

Who We Are

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips the brains of devices we use every day. As the foundation of the global electronics industry Applied enables the exciting technologies that literally connect our world like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology join us to deliver material innovation that changes the world.

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We are seeking an experienced Databricks Machine Learning (ML) Administrator to own the endtoend administration governance and secure operations of our ML environments on 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 costefficient ML platform.

Key Responsibilities

Platform Operations & Compute

• 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).
• Administer Jobs and Pipelines that orchestrate training evaluation and batch/realtime scoring; manage runas identities and default privileges to meet leastprivilege requirements.
• Establish and enforce compute access controls (attach/restart/manage) and workspace object permissions; standardize policies to prevent configuration drift.

ML Lifecycle Governance (MLflow & Serving)

• Govern MLflow Experiments and Registered Models with finegrained permissions (read/edit/manage) standardizing experiment tracking model versioning stage transitions and approvals.
• Operate and secure model serving endpoints including permissions for view query and manage actions; implement change control for deployments.

Data Access & Unity Catalog Alignment

• Coordinate with data governance to implement metastore catalog schema and tablelevel permissions that support feature engineering training and evaluation while safeguarding sensitive data.
• Apply enterprise identity and access management patterns across account and workspace scopes (users groups service principals) using SCIM/SSO standards.

Security Compliance & Auditability

• Enforce workspace object ACLs compute isolation modes secret handling and logaccess controls for ML clusters; implement Spark ACL settings per policy.
• Operationalize system tables/audit logs and usage analytics to meet regulatory and internal control requirements; partner with Security/GRC for periodic reviews.

Reliability Monitoring & Incident Response

• Monitor cluster health job success/failure serving endpoint SLOs and capacity; establish alerting and incident runbooks for ML infrastructure.
• Lead postincident reviews and continuous improvement for platform reliability and developer productivity.

Cost Management & FinOps

• Implement and iterate compute policies budget policies and usage dashboards to optimize GPU/CPU consumption for ML training and serving.

Enablement & Best Practices

• Define and evangelize ML platform standards: environment baselines cluster policies experiment hygiene model promotion flows and serving changemanagement.
• Partner with ML teams to align platform features (AutoML Feature/Vector stores model serving) to use cases and performance targets.

Required Qualifications

• 5 years administering Databricks or similar ML/data platforms (e.g. Sparkbased platforms) with handson experience in workspace administration compute policies and MLflow governance.
• Proven expertise managing Databricks permissions (workspaces clusters jobs experiments registered models serving endpoints) via UI REST/CLI.
• Strong understanding of Unity Catalog concepts and implementing catalog/schema/table access for ML workflows.
• Working knowledge of Python/Scala sufficient to understand notebooks init scripts and operational tooling (no application development required).
• Experience with SSO/SCIM enterprise identity providers and groupbased access patterns across account and workspace scopes.
• Familiarity with audit logging system tables and costmanagement techniques in Databricks.

Preferred Qualifications

• Databricks Platform Administrator accreditation (or equivalent) and experience with serverless/SQL warehouses cluster pools and model serving.
• Experience operationalizing runas service principals for jobs and pipelines and separating ownership vs. execution permissions.
• Exposure to infrastructureascode (e.g. Terraform) for permissions/policies and environment baselining.
• Understanding of data protection controls (masking row/column access) and secure handling of secrets and logs in ML contexts.

Tools & Technologies You Will Use

• Databricks Workspace & Account Console Unity Catalog Jobs Pipelines MLflow Model Serving Databricks Runtime for ML SQL Warehouses.
• Databricks CLI/REST APIs for permissions and automation; optional IaC (Terraform) for policy/permission as code.

Additional Information

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Relocation Eligible:

Applied Materials is an Equal Opportunity Employer. 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.