Tessera Labs
AI Engineer
San Jose Office (HQ) · Mid
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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.
- 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.
Responsibilities
- 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.
- Manage context deliberately. Enterprise artifacts are enormous and a forty-step run accumulates history fast
- 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.
- 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.
Requirements
- Hands-on experience with enterprise platforms, their APIs, and their extension models - SAP, Salesforce, Workday, Oracle, Snowflake, MuleSoft, ServiceNow.
Company info
- Governance: every action is logged, traceable, and reversible, because our customers are regulated and these are the systems that close their books.
- 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.
This listing is sourced directly from Tessera Labs's careers page and normalized into a canonical job model.