Member of Data Staff (Data Acceleration)

• 6+ years in data science, analytics engineering, data engineering, or a related role. You've been close enough to real data work to know what should and should not be automated.
• Deep SQL and analytics judgment - you can reason through metrics, experiments, data models, and messy warehouse reality without relying on a tool to think for you.
• Strong product sense - you understand what stakeholders actually need, what makes a workflow adoptable, and how to turn a prototype into a product people use.
• Hands-on LLM experience - you've built with frontier models, agents, RAG systems, evals, or AI-powered workflows and have opinions about where they work and where they fail.
• Pipeline and modeling fluency - you've worked with dbt, warehouse schemas, data quality issues, and the practical tradeoffs behind durable data systems.
• Builder mentality - you see a manual process and immediately think about how to systematize it. You ship fast, measure quality, and iterate.
• Autonomy - this is a new function. You'll help define the roadmap as much as execute it.

Bonus

• Experience building production AI agents or agent evaluation systems.
• Experience with Snowflake, semantic layers, or metadata systems.
• Experience building internal tools, Slack bots, CLIs, or developer productivity products that people actually used.
• Strong experimentation background, including metric design and statistical interpretation.
• Experience with BI tools and the judgment to know what should be automated versus kept human-reviewed.
• Early-stage startup experience.

Why This Role

• Set the standard for the industry - Perplexity's data team is already using AI across its work. You'll turn that into something other data teams look to as the benchmark.
• Build AI with AI - Perplexity builds AI for people who expect more. You'll bring that same level of ambition to how the company works with data.
• Frontier models, day one - you're at an AI company with access to frontier infrastructure and people who deeply understand what's possible.
• Massive leverage - the systems you build will multiply the output of every data team member and every stakeholder who needs data.
• Direct impact - small team, no layers of approval. Idea to shipped system in days, not quarters.