Member of Technical Staff (Search Crawler Analyst)
Summary
Perplexity is hiring a Search Crawler Analyst in Belgrade to diagnose quality issues in its web crawling pipeline, build metrics and data pipelines, and train small models (including LLM-as-a-judge setups) to improve crawl, parsing, and indexing quality. The role blends data analysis with production-grade engineering.
The internet is vast, containing trillions of URLs. Perplexity’s crawling and storage system is complex and has multiple stages (URL discovery, crawling, parsing, indexing). Each stage offers many opportunities for improvement and room for intricate bugs. You’ll work at the intersection of data analysis and engineering — designing metrics, building data pipelines, and improving the quality of our search and answer systems.
Responsibilities
Find and diagnose quality issues in our crawling pipeline
Train small models that optimize particular aspects of the pipeline (e.g. parsing quality)
Build datasets for model training, including LLM-as-a-judge labeling pipelines
Improve page selection algorithms for indexing
Design and analyze experiments to validate improvements
Qualifications
4+ years of experience as a data analyst, ML engineer, or in a related role
Strong coding skills: you should be able to write production-grade code at the level of a mid-level backend engineer
Experience designing metrics from scratch
Experience training ML models that shipped to production with measurable metric improvements
Nice to have
Direct experience working on web crawling or indexing pipelines
Skills
As published by ashby · 6 questions · 2 written answers
Basics
Name, Email, Resume, Location
Short answers (2)
- Phone Number
- Exercise Submission (Shared URL)
Pick from a list (2)
- Will you now or in the future require sponsorship for employment visa status in the United Kingdom, Germany or Serbia?
- Are you able to come into the office three days per week?
Written answers (2)
- What are the most interesting aspects of Perplexity that you are excited to work on?
- How do you use AI in your day-to-day work? What are some of the underappreciated benefits and/or pain points of your favorite AI tools?