Machine Learning Engineer, Content and Navigation

Whatnot · Consumer · San Francisco, CA · Engineering

Machine Learning Engineer on the Discovery Content and Navigation team, focusing on building and deploying ML models for personalized navigation, search, and recommendations in a high-growth consumer marketplace. The role involves end-to-end project ownership, from data collection to production deployment and experimentation, with a strong emphasis on applied ML methods for consumer-scale data.

What you'd actually do

  1. Lead the design, development, and productionization of ML models to capture intent and content signals that powers personalized navigational experience, search, and recommendations
  2. Lead ML-based projects from end-to-end: scoping and planning, data collection and feature engineering, model training and deployment, backend implementation, and online experimentation
  3. Support product initiatives like category and brand recommendations, promote high quality and relevant livestreams and products in feed and search.
  4. Work closely with teammates and cross-functional partners to implement ML-based solutions into production at scale
  5. Drive technical excellence and establish ML best practices across the team and org.

Skills

Required

  • Python
  • SQL
  • common ML frameworks
  • applied statistical and machine learning fields
  • search
  • recommendations
  • content understanding
  • natural language processing
  • large language models
  • building and deploying ML models to solve user problems at scale
  • applying practical methods to solve real-world problems on consumer scale data
  • communication and leadership skills
  • product instincts

Nice to have

  • generalist software development experience in high growth startups

What the JD emphasized

  • 4+ years of industry experience building and deploying ML models to solve user problems at scale
  • track record of applying practical methods to solve real-world problems on consumer scale data
  • shipping products and features lightning-fast

Other signals

  • ML models for personalization
  • productionization of ML models
  • end-to-end ML projects
  • ML-based solutions at scale
  • applied statistical and machine learning