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Tessera Labs
San Jose, California, United States; New York City, New York, United States
Source: Tessera Labs careers · View original posting
From Tessera Labs's posting. “We” and “our” refer to the employer.
Tessera Labs is a new category of enterprise software: an AI platform that changes how the world's largest companies run.
Every large enterprise carries the same weight — decades of accumulated process, data, and code that no longer match the business it has become. Changing any of it is a program measured in years and hundreds of millions of dollars, staffed by armies of consultants, and it fails more often than anyone admits. Most companies have quietly accepted this as the cost of being large.
We don't. Tessera is a transformation engine: a governed, multi-agent platform that understands an enterprise's process, data, and code as one connected system and changes it in weeks rather than years. We're vendor-agnostic by design — SAP, Salesforce, Workday, Oracle, Snowflake, MuleSoft — and tied to none of them.
Two things make this hard, and they're the reason the job is interesting. Governance: every action is logged, traceable, and reversible, because our customers are regulated and these are the systems that close their books. And generality: the platform has to work on landscapes it has never seen, at companies whose complexity is genuinely unique to them.
We sell a product, not a service.
Our people are here to make the product successful, not the other way around. If you've watched enterprise AI companies quietly become consultancies, that distinction is the one to press us on.
We raised a $60M Series A led by Andreessen Horowitz, with Foundation Capital, Myriad Venture Partners, and Osage University Partners participating.
Turning platform capability into outcomes a Fortune 500 will bet on has two halves — the systems that surround the model, and the model itself. This role owns the first.
You will own agents end to end: the harness they run in, the tools they call, the context they see, the guardrails around them, and the evals that tell us whether any of it is improving. Most of the difficulty is not model access. It's an agent reasoning across a landscape with nineteen years of undocumented decisions embedded in it, where one call silently returns a stale schema forty steps into a plan — and the work of making that failure legible, reproducible, and then impossible.
This is not a prototyping role. Everything you build gets pointed at systems a company's quarter close depends on.
If the model itself interests you more than the systems around it, look at Research Engineer — same team, other half of the problem.
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