Job Details

Perplexity is an AI-powered answer engine founded in December 2022 and growing rapidly as one of the world's leading AI platforms. Perplexity has raised over $1B in venture investment from visionary leaders and firms. Our objective is to build accurate, trustworthy AI that powers decision-making for people and assistive AI wherever decisions are being made. Today, curious people use Perplexity to answer more than 780 million queries every month.

Perplexity is seeking an experienced Software Engineer focusing on building the next-gen AI Foundation & Platform to help revolutionize the way people search and interact online. In this role, you'll help build Perplexity's end-to-end AI data, evaluation and personalization infrastructure and flywheel which powers almost all agent products.

Tech Stack: Spark | AWS Data Stack (S3, RDS, DynamoDB, Docker, EKS, Kinesis) | Pytorch | DynamoDB | Databricks | Snowflake | LLM APIs

Perplexity is rapidly scaling both in number of use cases and number of users. Perplexity's data stack powers scalable, personalized and fast answers for millions of people worldwide.

Responsibilities

• 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.

Qualifications

• 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