Member of Technical Staff

About Sixtyfour Give Sixtyfour a name, an email, or a domain, and we return a complete, verified profile of the person or company behind it. Our AI agents do the research a great analyst would. They follow leads across company sites, public records, and social platforms, cross-check what they find, and score how much each answer can be trusted. Sales, recruiting, and marketing teams use us to find the people and accounts other tools miss. We're backed by Y Combinator (X25). Most of the difficulty is in getting the answer right. Which of the 4,000 John Smiths is the one you asked about? Is that job title current, or two years old? Can you answer both for a million records overnight without the bill exploding? That's the work. How we work We design before we build. On most projects, the bulk of the time goes into understanding the problem, sketching the system, and arguing through failure modes and tradeoffs at a whiteboard. Once the design holds up, the code goes quickly, and customers usually see it within weeks. We keep a high bar, and everything we ship has to hold up at production scale. If the whiteboard is your favorite part of engineering, you'll like it here. What you'll work on

• OSINT agents at the core of the product: how they plan an investigation, which sources they check, how they settle conflicting evidence, and when they decide they're done.
• New data sources and tools for the enrichment engine.
• Evals that tell us whether a prompt, model, or agent change made results better or just different.
• Batching, caching, retries, and orchestration for jobs that enrich millions of records at a time.
• Features end to end, from design doc to production.

Who we're looking for You should have:

• Strong fundamentals. You understand concurrency, networking, databases, and APIs below the level of whatever library you're calling.
• Design judgment. Given a vague problem, you can break it apart, find where it will fail, and defend the design you pick.
• A habit of thinking about scale. You consider data volume, latency, concurrency, and cost before anyone asks, and you can point to systems where you had to.
• Something real you've shipped (a product, an open-source project, research, a hackathon build) that you can walk through decision by decision.
• Solid code in Python or TypeScript, and the ability to pick up the other quickly. Backend and AI work is Python; the product is TypeScript and React.
• Real interest in LLMs and agents, and opinions about how to build with them.
• The ability to work full-time, in person, in San Francisco.

Nice to have:

• OSINT experience: deep online investigations, identity resolution, username tracing, entity mapping. This one counts for a lot.
• Something you've built on LLMs or agents, like a research tool, a RAG app, a scraper, or an agent loop.
• Distributed systems, queues, workflow engines, or high-throughput pipelines.
• React or Next.js, especially data-heavy interfaces.
• Production experience with Postgres, Redis, search, and observability.
• A nose for answers that look right and aren't.

We don't care much about pedigree. If you took a non-traditional path or haven't used our stack, apply anyway; we'll teach you the tools. How you think about problems is the one thing we won't bend on. What you get

• Ownership of features that go to production and paying customers, with your name on them.
• Close review of your designs and code from engineers who build research agents, evals, and large-scale systems every day. Expect real pushback; it's how people get good here.
• Room to scope your own work, make the call, and watch it land.
• A generous budget for frontier models and dev tools. Use the best ones.
• Competitive salary and meaningful equity.
• Full Health, dental, and vision insurance
• Lunch in the office and team offsites.

The details

• Full-time, in person in San Francisco.
• Level: new grad through senior.

How to apply Send us something you built and be ready to go deep on it: the design you chose, the alternatives you threw out, and what breaks first at 100x the load. We're a small team, so every hire changes what the company can do.