Principal Machine Learning Engineer

About the role

Implements machine learning (ML) models for production, ensuring readiness for deployment, automating ML workflows, and creating monitoring infrastructure. Evaluates data quality, security, and privacy issues; troubleshoots ML infrastructure; collaborates on model integration; and develops internal tools and services. Maintains up-to-date knowledge of ML developments and integrates best practices into model development.

Responsibilities

• Model Productionization
  • Implements ML models for production using ML and software development expertise.
  • Transforms ML prototypes into production-ready models.
  • Collaborates with Development Leads, Product Management, Operations, and Release Management to make technical decisions and shape software delivery.
• Model Deployment
  • Ensures ML model readiness for deployment by scaling models, cleaning code, and meeting production quality standards.
  • Automates ML workflows from ETL to deployment and monitoring to enable continuous integration and delivery of ML solutions.
• Model Performance
  • Creates infrastructure and frameworks to monitor model performance and alignment with design criteria.
  • Proactively monitors deployed models and troubleshoots independently or with Data Science teams.
  • Develops novel metrics to provide analytical insights to non-technical stakeholders.
• Data Quality
  • Evaluates data quality (e.g., bias, fairness), security, and privacy issues, and mitigates their impact on modeling.
  • Performs data cleaning, preprocessing, and feature identification to prepare for model training.
• Model Integration and Operation
  • Collaborates with data scientists and software developers to integrate ML models into new or existing systems.
  • Maintains alignment between model development and operations for smooth deployment and continuous improvement.
  • Provides expert troubleshooting and debugging support; addresses issues in ML infrastructure and workflows.
• Tool Development
  • Develops, maintains, and refines internal tools, platforms, environments, and services.
• Coding and Documentation
  • Develops efficient, bug-free, medium-complexity code from scratch and maintains existing codebases.
  • Implements best practices for version control, code review, and deployment.
  • Builds and maintains professional documentation for technical processes (experimentation, data collection, analyses, model building).
  • Tests and reviews code for bugs.
• Machine Learning Expertise
  • Keeps current with ML developments and integrates knowledge into model development.
  • Evaluates third-party ML frameworks (e.g., PyTorch, TensorFlow, Keras) for performance, scalability, and production integration.
• Planning & Execution
  • Manages and coordinates moderately complex tasks, monitoring timelines and deliverables.
  • Delegates, monitors, and prioritizes work across multiple projects with technical oversight.
• Collaboration & Partnership
  • Collaborates across the organization to align on expectations and achieve shared objectives.
  • Leverages understanding of business leaders, stakeholders, and customers to ensure solutions meet their needs.
  • Supports inclusivity by actively seeking and listening to diverse perspectives.
• Problem Solving
  • Identifies and addresses moderately complex issues using data and standard practices.
  • Proactively escalates unresolved or critical issues with thorough assessments and potential solutions.
  • Reviews, contributes to, and documents problem-solving strategies.
• Continuous Learning
  • Pursues learning opportunities to expand knowledge and skills; stays abreast of industry trends.
  • Seeks and leverages ongoing feedback and training to improve skills.
  • Coaches and mentors junior team members to foster knowledge sharing.
• Continuous Improvement
  • Recommends and collaborates on process improvements to increase efficiency and effectiveness.
  • Evaluates impact on key stakeholders and solicits feedback on alternative approaches.
• Performance and Development
  • Participates in candidate interviews, assesses applicants, and provides hiring recommendations.

Qualifications

• Experience implementing ML models for production.
• Proficiency in transforming prototypes into production-ready models.
• Experience automating ML workflows (ETL to deployment and monitoring).
• Ability to design and maintain monitoring infrastructure for model performance.
• Experience evaluating data quality, security, and privacy issues and their impact on modeling.
• Strong collaboration skills with stakeholders across Development, Product, Operations, and Release Management.
• Experience developing internal tools, platforms, and services.
• Proficiency in writing efficient, bug-free, medium-complexity code and maintaining codebases.
• Knowledge of best practices for version control, code review, and deployment.
• Experience building and maintaining technical documentation.
• Familiarity with current ML developments and third-party frameworks (e.g., PyTorch, TensorFlow, Keras).
• Experience managing moderately complex tasks and coordinating across multiple projects.
• Ability to identify and address moderately complex issues using data and standard practices.
• Commitment to continuous learning, mentorship, and process improvement.

Pay

US: Hiring range in USD from $126,200 – $264,100 per year. May be eligible for bonus, equity, and compensation deferral. Range accounts for knowledge, skills, experience, market conditions, location, internal peer equity, products, industries, and lines of business.

Benefits

• Medical, dental, and vision insurance, including expert medical opinion
• Short-term and long-term disability
• Life insurance and AD&D
• Supplemental life insurance (Employee/Spouse/Child)
• Health care and dependent care Flexible Spending Accounts
• Pre-tax commuter and parking benefits
• 401(k) Savings and Investment Plan with company match
• Paid time off: Flexible Vacation for salaried employees; accrued vacation for others (13 days annually for first three years, 18 days thereafter; prorated for 20–34 hours/week)
• 11 paid holidays
• Paid sick leave: 72 hours upon hire; refreshes annually up to a 112-hour cap
• Paid parental leave
• Adoption assistance
• Employee Stock Purchase Plan
• Financial planning and group legal
• Voluntary benefits including auto, homeowner, and pet insurance