Senior Machine Learning Engineer

Senior Machine Learning Engineer will focus on implementing and productionizing machine learning model serving. The role emphasizes automation of ML workflows and the creation of monitoring and troubleshooting capabilities that support deployed models across production environments.

Key Responsibilities

• Implement machine learning (ML) model serving for production.
• Ensure ML model readiness for deployment by meeting production quality standards, scaling models, and cleaning model code.
• Automate ML workflows, including end-to-end serving pipelines from data extraction, transformation, and loading (ETL) through deployment and monitoring to enable continuous integration and continuous delivery of ML solutions.
• Build infrastructure and frameworks to monitor the performance of deployed ML models, including alignment with design criteria.
• Proactively monitor deployed model performance and troubleshoot independently or in collaboration with Data Science.
• Evaluate potential data quality, security, and/or privacy issues and assess their impact on modeling.
• Provide troubleshooting and debugging support, addressing issues in ML serving infrastructure and workflows and creating robust solutions to prevent recurrence.
• Collaborate with stakeholders to integrate ML models into new or existing systems.
• Develop, maintain, and refine tools, platforms, and services for internal use.
• Develop efficient, bug-free code from scratch, including medium-complexity implementations, while maintaining and organizing the existing codebase.
• Implement best practices for version control, code review, and code delivery/deployment.
• Maintain familiarity with current machine learning developments and incorporate relevant knowledge into model development.
• Develop novel metrics that provide analytical insights to non-technical stakeholders on how ML models are operating.
• Collaborate with multiple stakeholders (including data scientists and software developers) to integrate open source models into new or existing systems.
• Apply understanding of operational considerations for deployment, such as performance, scalability, stability, and maintenance.
• Test and review code for bugs; support documentation for technical processes, including experimentation, data collection and analyses, and model building.

Collaboration, Leadership, and Process Improvement

• Collaborate with stakeholders such as Development Leads, Product Management, Operations, and Release Management to make, adopt, and communicate technical decisions and influence software development and delivery.
• Manage and coordinate moderately complex tasks, monitoring timelines and deliverables for a moderately sized project or initiative.
• Delegate, monitor, and prioritize work across multiple projects while providing technical oversight and adjusting plans when resources or timelines change.
• Support inclusivity by actively seeking and listening to diverse perspectives and ensuring others feel heard and respected.
• Use analysis of a wide range of data and information to identify solutions in line with standard practices.
• Proactively escalate unresolved or critical issues with a thorough assessment and suggest potential solutions.
• Review, contribute to, and document problem-solving strategies.
• Develop, recommend, and collaborate on process improvements to increase efficiency and effectiveness across teams, and evaluate impacts on key stakeholders.
• Solicit feedback on alternative approaches to support continuous improvement.
• Coach and mentor junior team members to foster continuous learning and knowledge sharing within and across teams.
• Contribute to the talent development pipeline by participating in candidate interviews, assessing candidates, and providing hiring recommendations.

Technology and Tools

• PyTorch
• TensorFlow
• Keras

Location and Work Setting

Seattle, WA (onsite)