Member of Data Staff (AI Builder) at Perplexity
San Francisco, CA
$175,000–$330,000
Jul 22, 2026
San Francisco, CA
Jul 23, 2026
What this job asks for AI summary
This role sits at the intersection of data work and AI engineering, focused on building internal AI agents and systems that automate end-to-end data science workflows — from querying the warehouse and interpreting experiment results to detecting data quality issues and generating recommendations. It suits a seasoned data practitioner (6+ years) who has shifted toward building the AI-powered infrastructure and tooling that makes a small data team dramatically more productive.
Senior level · 6+ years · Full-time
Posted 2 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (5)
The role sits at the intersection of data science, AI/LLM engineering, and data engineering — it is genuinely hybrid. 15-2051 (Data Scientists) was chosen because the core framing is building AI systems that replicate and automate data science workflows (hypothesis formation, experiment analysis, metric design), but 15-1252 (Software Developers) is a strong runner-up given the emphasis on shipping production Python services, agents, and pipelines.
No work location is stated in the posting. Perplexity is headquartered in San Francisco, CA, but the JD does not specify a city, state, or remote policy — location fields are left blank accordingly.
LLM experience (frontier models, agents, RAG, evals) is listed under the required 'What We're Looking For' section and is treated as a hard gate, even though the specific sub-technologies (RAG, evals) are framed descriptively.
Snowflake, semantic layers, metadata systems, A/B/experimentation background, and BI tools all appear under the 'Bonus' section and are marked as preferred.
No compensation range is disclosed in the posting.
Read the full posting
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