Principal Machine Learning Engineer

The Principal Machine Learning Engineer role at Oracle focuses on implementing and productionizing machine learning models. This position owns deployment readiness and drives end-to-end operational excellence across monitoring, troubleshooting, workflow automation, and continuous improvement.

Key Responsibilities

• Implement machine learning (ML) models for production use and ensure deployment readiness.
• Automate ML workflows, including data extraction, transformation, and loading (ETL), as well as model deployment and monitoring, to support continuous integration and continuous delivery of ML solutions.
• Build infrastructure and frameworks to monitor model performance in deployment and alignment with design criteria.
• Proactively monitor deployed models and troubleshoot independently or in collaboration with Data Science.
• Evaluate potential data quality, security, and privacy issues and assess impacts on modeling and downstream data analysis.
• Support troubleshooting and debugging for ML infrastructure and workflows, including addressing root causes and implementing robust solutions to prevent recurrence.
• Transform ML prototypes into production-ready models, scaling models and cleaning model code to meet production quality standards.
• Develop novel metrics that provide analytical insights into model operating performance for non-technical stakeholders.
• Perform data cleaning, preprocessing, and feature identification to prepare data for model training.
• Collaborate with stakeholders to integrate ML models into new or existing systems, including Development Leads, Product Management, Operations, and Release Management.
• Maintain the partnership between model development and operations to enable smooth deployment and continuous model improvement.
• Develop, maintain, and refine tools, platforms, environments, and services for internal use, including professional documentation for technical processes (experimentation, data collection and analyses, and model building).
• Produce efficient, bug-free medium-complexity code from scratch, properly maintain and organize the existing codebase, and test and review code for defects.
• Apply best practices for version control, code review, and code delivery/deployment.
• Keep current with developments in the machine learning field and integrate relevant knowledge into model development, including evaluating third-party ML frameworks and libraries.
• Manage and coordinate moderately complex tasks, monitoring timelines and deliverables, and delegate, monitor, and prioritize work across multiple projects with technical oversight.
• Leverage understanding of business leaders, stakeholders, and/or customers to ensure proposed solutions meet needs, while seeking diverse perspectives to support inclusivity.
• Identify and address moderately complex issues by analyzing a wide range of information in alignment with standard practices, and proactively escalate unresolved or critical issues with thorough assessment and solution suggestions.
• Review, contribute to, and document problem-solving strategies; pursue continuous learning and proactively seek feedback and training.
• Coach and mentor junior team members and support knowledge sharing across teams.
• Recommend and collaborate on process improvements, evaluate their impact on key stakeholders, and solicit feedback on alternative approaches.
• Contribute to the talent development pipeline by participating in candidate interviews, assessing candidates, and providing hiring recommendations.

Required Technologies

• PyTorch
• TensorFlow
• Keras

Work Location

United States (onsite)