What you'll do

• Collaborate closely with AI Product, Applied ML, Post-Training, and Data Science teams to design, build, and maintain scalable data pipelines and data lakes
• Develop high-performance infrastructure that powers personalization features including memory, discover, and agentic products
• Create a scalable, multi-modal evaluation platform for all Perplexity AI products, including personalization, pro search, labs, deep research, and Comet
• Design tools and abstractions on foundational infrastructure to enhance personalization, analytics, recommendations, AI products, and post-training capabilities
• Holistically improve engineering foundation to support rapid growth of Perplexity products and international user base.

What you need

• Strong programming and data engineering skills, with proficiency in open source & distributed framework(AWS, Spark, Flink, Iceberg, DynamoDB)
• Familiarity with cloud-based data services (e.g., AWS, RDS, DynamoDB), containerized infrastructure (e.g., EKS, Docker), and data streaming (Flink, Spark streaming, CDC)
• Strong quantitative and engineering skills with experience in estimating performance at high scale
• Experience supporting various ML/AI engineering teams to build scalable frameworks to accelerate R&D for frontier models and AI products
• Experience iterating on improving LLM responses and set up proper evaluation framework or Judges to analysis performance holistically.
• Self-motivated with a strong sense of ownership of systems and designs
• 5+ years of industry experience in distributed systems or AI infrastructure

XML job scraping automation by YubHub