Member of Technical Staff (Data Scientist, Evals)

About the role

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

What you’ll do

• 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

What they’re looking for

Select a requirement to inspect fit, evidence or application context.

• 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

Helpful, not always essential

• 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