Principal Machine Learning Engineer

Oracle•Nashville, TN

1d•$126,200 - $264,100

About The Position

The Principal Machine Learning Engineer implements and productionizes machine learning model serving, preparing models for deployment and building automated workflows and infrastructure for monitoring model performance. The role evaluates potential data-quality, security, and privacy issues and their effects on modeling; provides troubleshooting and debugging support; and collaborates with stakeholders to integrate models into new and existing systems. It also develops, maintains, and refines internal tools, platforms, environments, and services, writes and maintains code, and applies current machine-learning engineering developments to model development and production. The role works across machine-learning modeling, deployment, performance monitoring, integration, operations, tool development, and documentation. It coordinates moderately complex work and priorities across projects, provides technical oversight, collaborates with stakeholders to meet shared objectives, and contributes to problem solving and process improvement. It also coaches and mentors junior team members and participates in candidate interviews and hiring recommendations.

Responsibilities

Implement machine-learning model serving for production using machine-learning and software-development knowledge.
Collaborate with Development Leads, Product Management, Operations, Release Management, data scientists, software developers, and other stakeholders to make and communicate technical decisions and integrate models into systems.
Prepare model serving for deployment by scaling models, cleaning model code, and meeting production-quality standards.
Automate model-serving workflows from ETL through deployment and monitoring to support continuous integration and delivery of machine-learning solutions.
Create infrastructure and frameworks to monitor deployed models and systems against performance and design criteria.
Monitor deployed model performance, troubleshoot issues independently or with Data Science, and develop metrics that communicate model performance to nontechnical stakeholders.
Address operational considerations such as performance, scalability, stability, and maintenance; provide expert troubleshooting and debugging support; and develop robust solutions to prevent recurring problems.
Develop, maintain, and refine tools, platforms, environments, and services for internal use.
Write efficient, bug-free code from scratch, maintain and organize existing code, apply version-control, code-review, and delivery/deployment best practices, and test and review code for bugs.
Build and maintain professional documentation for technical processes, including experimentation, data collection and analysis, and model building.
Stay current with machine-learning engineering developments and third-party frameworks, packages, and libraries, evaluating their performance and scalability and integrating them into production environments.
Coordinate moderately complex tasks and deliverables, prioritize and delegate work across projects, and provide technical oversight while adapting to resource or timeline changes.
Align with organizational stakeholders and understand business leaders', stakeholders', and customers' needs when proposing solutions; support inclusive collaboration and listen to diverse perspectives.
Analyze moderately complex issues, escalate critical or unresolved problems with assessments and potential solutions, and document and contribute to problem-solving strategies.
Pursue learning and feedback, coach and mentor junior team members, and share knowledge across teams.
Recommend and help implement process improvements, solicit feedback on alternatives, and evaluate impacts on stakeholders.
Participate in candidate interviews, assess candidates, and provide hiring recommendations.