Principal Machine Learning Engineer

Oracle is hiring a Principal Machine Learning Engineer in the United States (onsite) to implement and productionize machine learning models. This role focuses on end-to-end deployment readiness, automated ML workflows, monitoring and troubleshooting, and continuous improvement through ongoing learning and stakeholder collaboration.

Responsibilities

• Implement machine learning (ML) models for production and transform prototypes into production-ready solutions.
• Own deployment readiness for ML models by scaling models, cleaning model code, and meeting production quality standards.
• Automate ML workflows, covering the full path from data extraction, transformation, and loading (ETL) through model deployment and monitoring to support continuous integration and continuous delivery.
• Create infrastructure and frameworks to monitor deployed model performance and alignment with design criteria.
• Proactively monitor deployed models, troubleshoot independently or with Data Science, and address issues in ML infrastructure and workflows.
• Evaluate potential data quality, security, and privacy issues and assess impacts on modeling and analyses.
• Develop novel metrics that provide analytical insights to non-technical stakeholders about how well ML models are operating.
• Perform data cleaning, preprocessing, and feature identification to enable and improve model training.
• Collaborate with stakeholders to integrate ML models into new or existing systems, including coordination with Development Leads, Product Management, Operations, and Release Management.
• Develop, maintain, and refine tools, platforms, environments, and services for internal use, ensuring they support operational considerations such as performance, scalability, stability, and maintenance.
• Write efficient, bug-free code from scratch and properly maintain and organize the existing codebase, including testing and code reviews.
• Apply best practices for version control, code review, and code delivery/deployment.
• Maintain familiarity with current developments in machine learning and integrate new knowledge into model development.
• Build and maintain professional documentation for technical processes, including experimentation, data collection and analyses, and model building.
• Stay current with third-party ML frameworks, packages, and libraries (including PyTorch, TensorFlow, and Keras) to evaluate performance and scalability and support production integration.
• Manage and coordinate moderately complex tasks, monitor timelines and deliverables, and delegate, monitor, and prioritize across multiple projects with technical oversight.
• Leverage understanding of stakeholders and business needs to ensure solutions meet requirements.
• Support inclusivity by actively seeking and listening to diverse perspectives.
• Identify and address moderately complex issues using analysis in line with standard practices, and proactively escalate unresolved or critical issues with thorough assessment and potential solutions.
• Review, contribute to, and document problem solving strategies.
• Pursue learning opportunities, seek and leverage ongoing feedback and training, and coach and mentor junior team members.
• Recommend and help implement process improvements across teams, solicit feedback on alternative approaches, and evaluate impact on key stakeholders.
• Contribute to the talent development pipeline by participating in candidate interviews, assessing candidates, and providing hiring recommendations.

Technologies

• PyTorch
• TensorFlow
• Keras