TRAM Trainer

Every breakthrough in trade infrastructure has followed the same pattern: reduce a transaction cost, expand the market. Containerization for goods. SWIFT for money. Stripe for payments. Compliance is one of the last and largest — and the hardest, because trade rules aren't data to be looked up. They're a complex adaptive system with 190+ sovereign jurisdictions, in different languages, changing constantly, reacting to each other.

Sphere built the system that solves it. Our AI (TRAM) ingests global trade law, interprets it, resolves conflicts across jurisdictions, and produces compliance determinations more reliable than human experts. We handle the entire lifecycle — calculation, registration, filing, remittance — at millisecond latency with zero downtime.

Backed by a16z and YC. $21M Series A, 30%+ month-over-month growth, customers include ElevenLabs, Replit, Deel, Runway, and Lovable.

Small team, global surface area. Everyone owns a domain that would be a full team at a larger company. San Francisco, five days in office.

The problem keeps compounding. Expanding into input tax, withholding, e-invoicing, tariffs — each multiplies the complexity. Tens of millions of transactions today, billions ahead.

We are seeking an experienced indirect tax professional to train and continuously improve TRAM, our AI-driven tax engine. You will be the human check on the machine — reviewing TRAM's determinations and monitoring results at speed and volume, catching errors before they reach customers, and feeding structured findings back into the system so it gets smarter over time.

This is a hands-on review role, not an advisory one: you'll spend most of your time working through TRAM's output, one determination at a time, with sharp accuracy and fast turnaround. Over time, you'll also help shape the tax taxonomy underpinning the system and support deeper research where TRAM's findings need a second opinion.

WHAT YOU'LL DO

Review and validate TRAM-generated determinations at high volume, with speed and precision

Flag errors, edge cases, and recurring patterns, and translate them into clear, actionable feedback for TRAM to learn from