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San Jose, California, United States
Onsite
Not Available
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.
Design and ship the production agents that do transformation work: understanding a landscape, planning a change, executing it across process, data, and code, and proving it was correct.
Build the tool layer — typed, permissioned, well-documented interfaces that let agents read and act across enterprise systems without ever exceeding what a human approver authorized.
Improve agent performance through prompting, context construction, tool-use strategy, and decision logic — and know which of those a given failure calls for.
Build the retrieval layer over enterprise artifacts and metadata: chunking and indexing strategies for content that doesn't resemble prose, hybrid search, reranking, grounding, and the evaluation that tells you whether any of it helped.
Manage context deliberately. Enterprise artifacts are enormous and a forty-step run accumulates history fast; deciding what the model sees, what gets compressed, and what gets dropped is a first-class engineering problem here, not a prompt detail.
Use classical ML where it's the right tool. Plenty of the work inside an agent pipeline — routing, ranking, classification, anomaly detection, confidence estimation — is better served by a small supervised model than by another LLM call, and knowing the difference is part of the job.
Build the eval and monitoring layer: task sets drawn from real customer landscapes, regression coverage on every deploy, and alerting that catches a changed schema, a revoked authorization, or a new model version before the customer does.
Instrument every run so each model call, tool invocation, decision, and human approval can be reconstructed afterward. This is what "passes audit" means in practice, and it's a product requirement rather than a nice-to-have.
Diagnose production failures from execution traces down to root cause, then close the gap in the system rather than in a single prompt.
Design the guardrails, approval gates, and rollback paths that make an agent safe to point at a live enterprise landscape.
Build capability that generalizes. Anything that only works for one customer is a bug in the product, not a feature of the engagement.
Shipping the agent that lets a customer retire a third of their custom estate in a quarter — and the review surface that makes a human comfortable approving each call in minutes rather than days.
Designing the approval and write semantics for agents operating in a live landscape, so nothing reaches production without a traceable human decision and no partial failure leaves systems inconsistent.
Building the reconciliation agent that makes two merged companies' data agree, plus the eval set that tells us when it's confident and when it's guessing.
Standing up the replay-evaluation pipeline that scores a candidate agent version against a month of recorded runs — real system responses, mocked writes — so a regression is caught before anything is touched in a customer environment.
Taking a pattern that worked at one customer and turning it into a platform primitive that works at the next four without a human rewriting it.
Have 3+ years building and operating production software, with meaningful recent time on LLM-powered systems.
Have shipped agentic systems that real users depend on, and have owned the incident when they broke.
Are fluent in the current agent toolkit — tool calling, orchestration, context engineering, RAG, evals, tracing — and can say where each one stops working.
Have built retrieval systems against messy real-world corpora, and have measured them rather than assumed them.
Have real traditional ML in your background — supervised learning, feature engineering, model evaluation — and reach for it when it beats a prompt.
Treat evaluation as engineering rather than a report you generate at the end.
Think in systems and customer outcomes, not model metrics.
Are comfortable with non-determinism and have opinions about building reliably on top of it.
Write strong Python and are comfortable in TypeScript.
Ship fast, and verify before you claim it works.
Find large, ugly, undocumented enterprise systems interesting rather than beneath you. That instinct is most of the job, and it's rarer than it should be.
Hands-on experience with enterprise platforms, their APIs, and their extension models — SAP, Salesforce, Workday, Oracle, Snowflake, MuleSoft, ServiceNow.
Experience with knowledge graphs, ontologies, or semantic models over messy real-world systems.
Experience with code analysis, program transformation, or automated refactoring at scale.
Experience with MCP, sub-agents, or agent skill/plugin architectures.
A background in distributed systems or workflow engines, where partial failure is the default case.
Experience at an early-stage startup or as a founder, where scope was whatever needed doing that week.
Familiarity with enterprise security and compliance realities: SSO, RBAC, segregation of duties, PII handling, SOC 2, data residency.
We're looking for someone who enjoys building reliable, scalable systems that help teams move faster. If you take pride in cutting-edge AI, value clear ownership, and want to have a meaningful impact in a fast-moving environment, you’ll fit right in.
No third party may recruit, solicit candidates, publish job opportunities, use Tessera Labs’ name or branding, or represent that they are acting on behalf of Tessera Labs without prior written authorization. Any such activity conducted without explicit written consent is strictly prohibited.
