Member of Technical Staff (Data Scientist

ONE STOP COLLECTIBLE CORP

San Francisco, CA, United States

1 day ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience required

1 year minimum

Working hours

Regular working hours

Job source

Amazon Web Services Python (Programming Language) Machine Learning SQL Databases Large Language Models Production Code Databricks

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

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, * 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