# AI Engineer at Tessera Labs

Tessera Labs is hiring an AI Engineer for a remote role open to applicants in United States. It pays $200k-$250k a year and Level rates it Builds AI ●●●●; you can [apply on Tessera Labs](https://jobsbylevel.com/go/23b5d0eb-f8a3-4dbe-86e8-18e4a10c1d2e?utm_medium=ai-agent).

AI Level 4, AI centrality 91 out of 100. Remote (San Jose Office (HQ)).

## Details

- Company: [Tessera Labs](https://jobsbylevel.com/companies/tessera-labs)
- AI level: AI Level 4 (score 91 out of 100)
- Location: Remote (San Jose Office (HQ))
- Salary: $200k-$250k
- Posted: August 21, 2026
- Apply: https://jobsbylevel.com/go/23b5d0eb-f8a3-4dbe-86e8-18e4a10c1d2e?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=ai-engineer-at-tessera-labs-e6baee

## Prepare for this job

The job page lists 4 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/ai-engineer-at-tessera-labs-e6baee

## Description

About Tessera Labs 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. About the role 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. What you'll do 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

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