Member of Technical Staff

Sixtyfour AI Inc.; San Francisco (CA); On-site; USD 140,000 - 220,000; Full time

Benefits offered by this job

Health, dental, vision insurance

Lunch in office and team offsites

Competitive salary and equity

Job summary

Sixtyfour AI Inc. is hiring in San Francisco for engineers who design large-scale OSINT agents and data-enrichment tools. You’ll work on sources, evidence reconciliation, and scalable batch processing to support millions of records.

Strong fundamentals in concurrency, networking, databases, and APIs are required, with Python or TypeScript for backend and frontend tasks. In-person, full-time in SF is expected.

Qualifications

• Must understand concurrency, networking, databases, and APIs.
• Experience with Python or TypeScript is preferred.
• Strong design judgment and ability to scale systems.

Responsibilities

• OSINT agents at the core of the product: plan investigations and resolve conflicting evidence.
• Develop data sources and tools for the enrichment engine.
• Build and maintain scalable batch processing and orchestration for large datasets.
• Contribute end-to-end features from design to production.

Skills

Concurrency fundamentals

Networking basics

Databases

APIs

Python

TypeScript

React

In-person SF work

Tools

Postgres

Redis

Search

Observability

Job description

About Sixtyfour

Sixtyfour builds research infrastructure for decisions that can’t afford to be wrong. Our AI agents investigate people and companies across public records, web pages, archives, and other sources, returning structured findings with supporting evidence and a graph of the connections between them.
We help teams investigate fraud, research the people behind accounts, and understand who they’re doing business with. Start with an email, a username, or a company domain. Sixtyfour follows the evidence, connects records across sources, and gives teams findings they can inspect and use in their workflows.
The hard problems are in making that research reliable at scale. Do two profiles belong to the same person? What happens when sources disagree? Does a shared identifier reveal a meaningful connection? How do you investigate millions of records while keeping latency and cost under control? These are the problems we’re building for.
We’re backed by Y Combinator (X25)

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 hasn'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.