Principal Machine Learning Engineer

Oracle•Nashville, TN

95d

About The Position

The Principal Machine Learning Engineer uses machine learning and software development expertise to bring machine learning models from prototype to production. The role prepares models for deployment by scaling and cleaning code, meeting production quality standards, and automating workflows from data extraction, transformation, and loading through deployment and monitoring. The engineer builds frameworks and infrastructure to monitor model performance and alignment with design criteria, proactively troubleshoots deployed models, and develops metrics that explain model performance to non-technical stakeholders. The position also addresses data quality, bias, fairness, security, and privacy, and supports data cleaning, preprocessing, and feature identification. Working with data scientists, software developers, development leads, product management, operations, and release management, the role integrates models into systems and supports their deployment and ongoing improvement. It develops and maintains internal tools, platforms, environments, and services; writes and maintains code; applies version-control, code-review, and delivery practices; and documents and tests technical work. The engineer stays current with machine-learning developments and frameworks, coordinates work across projects, contributes to process improvement, mentors junior colleagues, and participates in candidate interviews and hiring recommendations.

Responsibilities

Implement machine-learning models for production and transform prototypes into production-ready models.
Collaborate with technical and business stakeholders to make, adopt, and communicate technical decisions and shape software development and delivery.
Prepare models for deployment by scaling them, cleaning code, and ensuring production quality standards are met.
Automate machine-learning workflows, including ETL, deployment, and monitoring, to support continuous integration and delivery.
Create infrastructure and frameworks to monitor model performance and alignment with design criteria.
Monitor deployed models, troubleshoot issues independently or with Data Science, and develop metrics that communicate model operation to non-technical stakeholders.
Assess data quality, bias, fairness, security, and privacy risks, and minimize their impact on analysis and modeling.
Clean and preprocess data and identify features to prepare for model training.
Integrate machine-learning models into new or existing systems and maintain coordination between model development and operations.
Account for performance, scalability, stability, and maintenance in model deployment; provide troubleshooting and debugging support and develop solutions to prevent recurring issues.
Develop, maintain, and refine internal tools, platforms, environments, and services.
Write, maintain, and organize code; apply version-control, code-review, and code-delivery practices; document technical processes; and test and review code for bugs.
Maintain familiarity with machine-learning developments and evaluate and integrate third-party frameworks, packages, and libraries into production environments.
Coordinate moderately complex work, monitor project timelines and deliverables, and delegate, prioritize, and adjust work across multiple projects.
Collaborate across the organization and use stakeholder and customer understanding to ensure solutions meet needs; support inclusivity by listening to diverse perspectives.
Analyze and address moderately complex problems, escalate unresolved or critical issues with assessments and suggested solutions, and document problem-solving strategies.
Pursue learning and feedback, stay current with industry trends and best practices, and coach and mentor junior team members.
Recommend and implement process improvements, solicit feedback on alternative approaches, and evaluate effects on stakeholders.
Participate in candidate interviews, assess candidates, and provide hiring recommendations.