Build the next generation ML systems that transform the way people find what they love.
Focus on building, productionizing and scaling machine learning models, and promoting engineering excellence.
Work with data scientists and lead cross-functional collaboration with Strategy, Product, Merchandising, Marketing, and Engineering partners to enhance capabilities.
Drive step-change improvements in machine learning models through novel research, large-scale experimentation, and production deployment.
Improve how we serve real-time recommendations in production at scale.

Requirements

5+ years of experience developing or improving machine learning models and taking them from prototype to production.
5+ years of advanced python development experience, including familiarity with data manipulation using spark and SQL.
Solid foundation in machine learning, plus a bonus of evaluating recommender systems.
Familiarity with modern ML frameworks (e.g. PyTorch, TensorFlow) and model deployment frameworks leveraging container orchestration.
Hands-on experience with observability tools (e.g., Datadog, Prometheus) and a deep understanding of how to measure and optimize system latency.
Ability to translate complex technical concepts into clear, actionable insights for cross-functional partners, including Product, Design, and Engineering, to build consensus and drive projects forward.
Driven by curiosity to explore new technologies and take calculated risks, with a focus on identifying and implementing best practices that elevate the team's engineering standards.

Benefits

Medical, dental and vision benefits
Annual bonus
Restricted stock units
Comprehensive compensation packages
Inclusive health and wellness benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learning modelspythondata manipulationsparkSQLrecommender systemsPyTorchTensorFlowmodel deployment frameworkscontainer orchestration

Soft Skills

cross-functional collaborationtranslating technical conceptsbuilding consensusdriving projects forwardcuriosityexploring new technologiestaking calculated risksimplementing best practicesengineering standards