Senior Machine Learning Engineer

Oracle•Seattle, WA

4d•$87,800 - $209,500

About The Position

The Senior Machine Learning Engineer implements and prepares machine learning models for production serving, automates machine learning workflows, and builds infrastructure and frameworks for monitoring deployed model performance. The role evaluates potential data quality, security, and privacy issues and their impacts on modeling, and provides troubleshooting and debugging support for machine learning serving infrastructure and workflows. The engineer collaborates with stakeholders—including development leads, product management, operations, release management, data scientists, and software developers—to make technical decisions and integrate open-source models into new or existing systems. The role develops, maintains, and refines internal tools, platforms, environments, and services; writes and maintains code and documentation; and keeps up with developments in machine learning engineering and third-party frameworks such as PyTorch, TensorFlow, and Keras. The position also coordinates moderately complex work and multiple projects, contributes to problem solving and continuous improvement, supports inclusive collaboration, mentors junior team members, and participates in candidate interviews and hiring recommendations.

Requirements

Knowledge of machine learning and software development for implementing production model serving.
Ability to prepare models for deployment, automate model-serving workflows, and monitor deployed model performance.
Ability to collaborate with technical and business stakeholders and integrate models into existing or new systems.
Ability to troubleshoot and debug machine learning infrastructure and workflows.
Ability to develop and maintain code, internal tools, platforms, services, and technical documentation.
Familiarity with machine learning engineering developments and third-party machine learning frameworks, packages, and libraries, including PyTorch, TensorFlow, and Keras.
Ability to coordinate moderately complex tasks and prioritize work across multiple projects.
Ability to analyze moderately complex issues, communicate findings, and contribute to process improvements.

Responsibilities

Implement machine learning model serving for production using machine learning and software development knowledge.
Collaborate with development leads, product management, operations, release management, and other stakeholders on technical decisions and software delivery.
Prepare models for production 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.
Build infrastructure and frameworks to monitor trained model and system performance against design criteria.
Monitor deployed model performance, troubleshoot issues independently or with Data Science, and develop metrics that communicate model performance to non-technical stakeholders.
Integrate open-source models into new and existing systems with data scientists, software developers, and other stakeholders.
Address operational considerations such as performance, scalability, stability, and maintenance in model deployment.
Provide troubleshooting and debugging support for machine learning engineering infrastructure and workflows, and develop robust solutions to prevent recurring problems.
Develop, maintain, and refine tools, platforms, environments, and services for internal use.
Write efficient, bug-free, medium-complexity code from scratch; maintain and organize existing code; and test and review code for bugs.
Apply best practices for version control, code review, and code delivery and deployment.
Create and maintain professional documentation for technical processes, including experimentation, data collection and analysis, and model building.
Keep current with machine learning engineering developments and evaluate and integrate third-party machine learning frameworks, packages, and libraries into production environments.
Coordinate moderately complex tasks, monitor timelines and deliverables, and delegate, prioritize, and oversee work across multiple projects.
Collaborate across the organization, understand stakeholder and customer needs, and support inclusive participation by seeking and listening to diverse perspectives.
Analyze moderately complex issues, identify solutions, escalate unresolved or critical issues with assessments and potential solutions, and document problem-solving strategies.
Pursue learning and feedback, stay informed about industry trends and best practices, and coach and mentor junior team members.
Recommend and help implement process improvements, solicit feedback on alternative approaches, and evaluate impacts on stakeholders.
Participate in candidate interviews, assess candidates, and provide hiring recommendations.