Research Scientist

The posting, as Tessera Labs wrote it

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 research 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 — which is also why research here is a durable investment rather than a line item on an engagement.

We raised a $60M Series A led by Andreessen Horowitz, with Foundation Capital, Myriad Venture Partners, and Osage University Partners participating.

ABOUT THE ROLE

We're looking for a Research Scientist to set and pursue a research agenda for reliable long-horizon agents operating inside real enterprises.

Frontier labs optimize for general capability, and the public agent benchmarks are mostly sandboxes. Very little rigorous work exists on what it takes for an agent to reason across a system with nineteen years of undocumented decisions in it, plan a change across forty coupled steps, recover when step twelve reveals the model of the world was wrong, and be right often enough that a CFO signs the go-live. Almost nobody has the landscapes, the traces, or the customers to study it. We do.

Two properties make this an unusually good research setting. First, much of the task space is verifiable — a transformation either produces a system that builds, passes regression, and behaves equivalently, or it doesn't. That's a real reward signal, not a preference model. Second, the parts that aren't verifiable are where the interesting work is: is this reconciliation correct, or merely plausible? Was retiring that capability the right call? Designing reward and evaluation across that boundary is the central research question here.

You'll invent methods rather than only apply them, work with Research Engineers who help you run at scale, and hear from a product team within weeks whether you were right.

We'd like you to publish. Not everything, and never at t
[…]
xt reasoning, knowledge representation and structured reasoning, or evaluation methodology. Depth in RL tuning is the single strongest signal for this role.

• Are hands-on. You write the code, run the experiments, and read the logs. This is not a role that directs research from a distance.
• Have exceptional research judgment — you pick the question well and kill your own ideas quickly when the evidence says to.
• Hold rigor and urgency at once. We need results that are true and results that arrive.
• Write clearly, and enjoy explaining a technical argument to people who aren't researchers.

STRONG CANDIDATES MAY ALSO HAVE

• Experience owning a research direction end to end at a frontier lab or a strong academic group.
• Published work on agents, tool use, RL for LLMs, code generation or repair, reasoning, or evaluation.
• Experience with verifiable-reward RL, or with the failure modes of reward models where verification is incomplete.
• Experience with knowledge representation, ontologies, or neurosymbolic approaches to structured domains.
• Experience with training runs at meaningful scale, including the ones that failed for three weeks.
• A history of mentoring researchers and engineers into better work.
• Curiosity about enterprises as a research domain. The problems here are strange and specific, and they reward people who find that interesting.

JOIN THE FUTURE OF AI AT TESSERA LABS

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.