About the Role
Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and our specialized data sources. We aim to use the latest models as they are released, but the intelligence frontier is a jagged one, and popular benchmarks do not effectively cover our use cases. In this role, you will build specialized evals to improve answer quality across Perplexity, covering search-based LLM answers and other scenarios popular with our users.
Responsibilities
- Architect and maintain automated evaluation pipelines to assess answer quality across Perplexity's products, ensuring high standards for accuracy and helpfulness
- Design evaluation sets and methods specifically to measure the impact of tool calls (particularly web search retrieval) on the final answer's quality
- Develop VLM-based solutions to programmatically evaluate how final answers render visually across different platforms and devices
- Continuously review public benchmarks and academic evaluations for their applicability to the Perplexity product, adapting and incorporating them into our regular performance measurements
- Operate within a small, high-impact team where your evaluation metrics directly shape product changes, collaborating closely with technical leadership to measure and improve Answer Quality
Qualifications
- PhD or MS in a technical field or equivalent experience
- 4+ years of experience in data science or machine learning
- Strong proficiency in Python and SQL (expected to write production-grade code)
- Experience building within a modern cloud data stack, specifically AWS and Databricks
- Comfortable with agentic coding workflows and using AI-assisted development tools to iterate faster
Preferred Qualifications
- 1+ years of experience working with LLMs at scale, specifically with LLM-as-a-judge setups
- Prior experience working on customer-facing web products or consumer apps, with real user traffic at scale
- A strong research background, with experience applying research methods to real-world ML problems
- Experience defining evaluation metrics (e.g., factual consistency, hallucination rate, retrieval precision) and building ground truth datasets