turbopuffer is looking for someone to open doors. You'll be the first conversation our future customers have with us. Your job is to find the right people, start human conversations, and get them talking to the team.
what you'll do:
find engineers and teams who are building with vectors or hitting limits with their current search
write outreach that developers actually want to read
qualify inbound leads and route them to the right people
research accounts deeply before reaching out—understand their stack
book meetings that don't waste anyone's time
track what's working and kill what isn't
live in communities where our customers hang out
design, execute, and analyze new outbound experiments to expand funnel
must have:
builder - you can build your own tooling, automate your own workflows.
typist – you write cold emails that don't sound like cold emails
technical – you can skim a company's GitHub or docs and understand what they're building
systematic – you build repeatable processes and actually follow them
human – you know the difference between persistence and spam
urgent – you move fast and don't let leads go cold
independent – you figure things out without waiting to be told
nice to have:
you've sold to developers before and know why they ignore most outreach
familiarity with databases, search, or infrastructure software
experience with prospecting tools like Clay, Apollo, or similar
comfort reading API docs or technical blog posts
you've built something yourself
tpuf is hardcore & whimsical. we value:
overstep > understep
show > tell
complexity is earned
showing up as part of our customer’s team
making stuff easy to react to
Skills Required
- Ability to build own tooling and automate workflows
- Write effective cold emails
- Skim GitHub or documentation to understand products
- Build repeatable processes and follow them
- Understand the difference between persistence and spam
- Ability to move fast and manage leads
- Independently figure things out
What We Do
turbopuffer is a serverless search engine that combines vector and full-text search using object storage. It is designed to make large amounts of data searchable for AI applications, offering high scalability, performance, and cost-efficiency by separating compute and storage. It serves as a critical infrastructure layer for companies needing to retrieve context for AI models, handling billions of documents with low latency.
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