Senior ML Engineer: Production-Ready Models & Automation

Oracle; Santa Clara (CA); On-site; USD 115,000 - 235,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) with company match

Employee Stock Purchase Plan

Paid time off

Job summary

Oracle is seeking a machine learning engineer to implement ML models for production, drive production readiness, and automate ML workflows. The role collaborates with data scientists and software teams to deploy scalable ML solutions and monitor performance.

Responsibilities include model deployment readiness, infrastructure collaboration, and ongoing improvements to ML pipelines, with a focus on practical, production-grade coding and maintaining documentation.

Responsibilities

• Utilizes ML and software development knowledge to implement ML models for production with minimal guidance.
• Transforms machine learning prototypes into production-ready models.
• Collaborates with Development Leads, Product Management, Operations, and Release Management to communicate technical decisions and shape software delivery.
• Ensures ML model readiness for deployment by scaling models, cleaning code, and maintaining production quality standards.
• Automates ML workflows from data extraction/ETL to deployment and monitoring to enable CI/CD of ML solutions.
• Monitors deployed models' performance and troubleshoots with DS or independently.
• Identifies data quality, security, and privacy issues and minimizes their impact on modeling.
• Leads collaboration with data scientists and software developers to integrate ML models into systems.
• Develops and maintains tools, platforms, and services for internal use.
• Writes efficient, bug-free code and maintains codebase with proper docs and CI practices.
• Keeps up with ML advances and integrates frameworks (e.g., PyTorch, TensorFlow) into production.

Job description

Oracle is seeking a machine learning engineer to implement ML models for production, drive production readiness, and automate ML workflows. The role collaborates with data scientists and software teams to deploy scalable ML solutions and monitor performance.

Responsibilities include model deployment readiness, infrastructure collaboration, and ongoing improvements to ML pipelines, with a focus on practical, production-grade coding and maintaining documentation.