Asks for

The ad says“Payments don't wait for compliance. We process risk signals at the same speed money moves – millions of transactions, thousands of entities, sub-second decisions. Doing this right on AWS means distributed inference, caching, queue orchestration, and self-healing data pipelines.”
The ad says“Payments don't wait for compliance. We process risk signals at the same speed money moves – millions of transactions, thousands of entities, sub-second decisions. Doing this right on AWS means distributed inference, caching, queue orchestration, and self-healing data pipelines.”
The ad says“Most financial crime data isn't accessible via APIs – it lives in legacy systems, government portals, and third-party platforms. We're building agents that navigate these systems like humans do: clicking, scrolling, interpreting unstructured data, and adapting to interface changes. This requires computer vision, DOM understanding, and robust error handling at scale.”
The ad says“Payments don't wait for compliance. We process risk signals at the same speed money moves – millions of transactions, thousands of entities, sub-second decisions. Doing this right on AWS means distributed inference, caching, queue orchestration, and self-healing data pipelines.”
The ad says“Most financial crime data isn't accessible via APIs – it lives in legacy systems, government portals, and third-party platforms. We're building agents that navigate these systems like humans do: clicking, scrolling, interpreting unstructured data, and adapting to interface changes. This requires computer vision, DOM understanding, and robust error handling at scale.”
The ad says“Most financial crime data isn't accessible via APIs – it lives in legacy systems, government portals, and third-party platforms. We're building agents that navigate these systems like humans do: clicking, scrolling, interpreting unstructured data, and adapting to interface changes. This requires computer vision, DOM understanding, and robust error handling at scale.”
The ad says“Out-of-the-box deep research agents easily confuse entities that look similar. ChatGPT isn’t going to be able to tell us whether John Smith is a terrorist. We’re building our own deep research pipeline to ensure LLMs don’t mix up facts from different people/companies.”
The ad says“Payments don't wait for compliance. We process risk signals at the same speed money moves – millions of transactions, thousands of entities, sub-second decisions. Doing this right on AWS means distributed inference, caching, queue orchestration, and self-healing data pipelines.”
The ad says“Payments don't wait for compliance. We process risk signals at the same speed money moves – millions of transactions, thousands of entities, sub-second decisions. Doing this right on AWS means distributed inference, caching, queue orchestration, and self-healing data pipelines.”

tl;dr: We’re automating compliance for banks and fintechs with AI agents that work like human analysts – on a browser. Demand is exploding, and we’re expanding our engineering team to move faster & meet our 6-month roadmap. If you love building end-to-end systems, shipping fast and tackling hard problems, we should talk.

PS: want to skip the queue? Save Morty from Rick in our AI-proof coding challenge. If things go well, we'll reach out to organize an interview directly with our CTO. And if you win, we pay you $10k, no questions ask.

Background

Before you can move money or open an account, someone has to make sure you’re not a criminal. That’s compliance – and it’s broken.

Sphinx builds AI analysts that work inside browsers, APIs, and internal systems just like humans — automating AML, KYC, KYB, and transaction monitoring end-to-end.

Why Join Sphinx

Massive, unsexy market: Banks spend $50B a year hiring thousands of analysts to review alerts and fill out reports by hand. The space has seen very little innovation, and customers complain about our most well-funded competitors weekly. It's not glamorous – until you realize you're building the intelligence layer that protects the entire financial system.
We have never lost a deal to a competitor: Yes you heard that right, we’ve won every RFP we’ve been in.
Small team, big surface area: Our co-founders previously sold an AI company & have built ML systems that scaled to millions of users and closed deals with major banks. The rest of our team in SF includes PhDs, industry experts, and competitive coders.
Hard tech: we’re going from zero to one; our tech is our moat.
Zero startup bullshit: we’re not chasing investors, we’re chasing customers. We’re trusted by leading banks and fintechs across the U.S., Canada, Europe, and LatAm. They’ve seen 90% less manual work, 4× lower costs.

Technical Challenges

1. Browser Agents for the Invisible Web

Most financial crime data isn't accessible via APIs – it lives in legacy systems, government portals, and third-party platforms. We're building agents that navigate these systems like humans do: clicking, scrolling, interpreting unstructured data, and adapting to interface changes. This requires computer vision, DOM understanding, and robust error handling at scale.

2. The Global Risk Graph

Risk doesn’t stop at borders, and neither can we. Our agents operate across languages and regulations, reconciling entities hidden behind shell structures and mismatched disclosures. Each check enriches a single, evolving graph of people, companies, and signals — a global risk intelligence network that never forgets.

3. Decisions at the Speed of Money

Payments don't wait for compliance. We process risk signals at the same speed money moves – millions of transactions, thousands of entities, sub-second decisions. Doing this right on AWS means distributed inference, caching, queue orchestration, and self-healing data pipelines.

4. Deep Research without Hallucinations

Out-of-the-box deep research agents easily confuse entities that look similar. ChatGPT isn’t going to be able to tell us whether John Smith is a terrorist. We’re building our own deep research pipeline to ensure LLMs don’t mix up facts from different people/companies.

What We Look For

You solve problems end-to-end: Our team owns features vertically. You'll architect agent behaviors, build backend systems, optimize ML pipelines, and ship product features – often in the same week.
You have a high bar for quality: In financial crime, "mostly working" isn't good enough. You obsess over edge cases, build robust error handling, and ship systems that institutions trust with billions in transactions.
You want to push AI agent capabilities: We're at the frontier of what's possible with AI agents in production. You'll experiment with the latest models, design novel agentic workflows, and solve problems that don't have Stack Overflow answers.
You communicate with clarity: We move fast with minimal process. You need to articulate tradeoffs, communicate blockers, and make technical decisions with limited information.
You're energized by impact: Our customers protect the financial system from criminals. Your code directly prevents money laundering and terrorism financing. That matters.
You hate meetings: You would much rather focus your time on building, being productive, and shipping code

About the Interview

Intro call (fit & motivation)
Technical screening / take-home
Deep-dive technical interview (no live coding)
Short paid in-person trial
Offer 🎉

Benefits

🍽 Lunch, dinner and snacks at the office
🏥 Comprehensive healthcare
🏝️ Unlimited PTO
🗺️ Annual offsite (last one in Mexico)!
✈️ Paid relocation & one-month temporary housing
💻 Latest tech and as many monitors as you can fit on a desk