Machine Learning Engineer II

Pinterest Pinterest · Consumer · Dublin, Ireland · Core Engineering

Machine Learning Engineer II at Pinterest focused on developing and implementing ML models for user targeting and personalization to drive engagement and growth. The role involves building scalable ML pipelines, conducting A/B tests, analyzing data, and contributing to ML infrastructure, with an emphasis on leveraging AI tools for engineering practices.

What you'd actually do

  1. Develop and implement ML models to improve user targeting and personalization for growth initiatives
  2. Design and build scalable ML pipelines for data processing, model training, and deployment
  3. Collaborate with cross-functional teams to identify potential ML solutions for growth opportunities
  4. Conduct A/B tests to evaluate the performance of ML models and optimize their impact on key growth metrics
  5. Analyze large datasets to extract insights and inform decision-making for user acquisition and retention strategies

Skills

Required

  • Python
  • PyTorch
  • SQL
  • Spark
  • Hadoop
  • recommendation systems
  • user modeling
  • personalization algorithms
  • statistical analysis
  • AI coding assistants
  • agentic tools

Nice to have

  • Natural Language Processing (NLP)
  • data visualization
  • AWS
  • Docker
  • Kubernetes
  • open-source ML projects
  • research publications

What the JD emphasized

  • 3+ years of experience applying ML to real-world problems, preferably in a growth or user acquisition context
  • Strong programming skills in Python and experience with PyTorch
  • Proficiency in data processing and analysis using tools like SQL, Spark, or Hadoop
  • Experience with recommendation systems, user modeling, or personalization algorithms

Other signals

  • Develop and implement ML models to improve user targeting and personalization for growth initiatives
  • Design and build scalable ML pipelines for data processing, model training, and deployment
  • Conduct A/B tests to evaluate the performance of ML models and optimize their impact on key growth metrics
  • Contribute to the development of our ML infrastructure, ensuring it can support rapid experimentation and deployment