ToBeVerified

Runtime QA for the agentic era

PrototypeAI/ML

Runtime verification for non-deterministic LLM outputs. Live reviewer agents plus an RL labeling loop catch generative-UI and agent failures at enterprise scale, before they reach users.

0 builders0 likesCreated Apr 23, 2026

PPrasanna Vinjamuri

Catch non-deterministic LLM failures before they reach the end user, at enterprise scale.

EngineeringSteady — flexible pace

What you'll learn

The gap between "LLM demo works" and "LLM survives enterprise traffic" — that gap is the whole field, and you'll live in it.

Build the live reviewer agents that verify enterprise LLM outputs in flight — generative UIs, agent traces, tool calls. You'll design reviewer prompts, measure inter-agent agreement, and tune the pipeline on real enterprise traffic.

Who you'll work with

Work alongside the TBV founder on a problem A16Z-portfolio CPOs are already naming on stage. Enterprise-grade scale, startup-grade speed.

TokensFounder referencePortfolio piece

Helpful:Python or TypeScript

Turn every reviewer correction into a stronger verifier — compound quality over time.

EngineeringSteady — flexible pace

What you'll learn

Modern RL-on-LLM systems in a real product context — not a toy benchmark. Ship a flywheel that measurably improves each week.

Build the reinforcement-learning labeling loop that turns reviewer decisions into improved verifier models. You'll own the data flywheel: reviewer output → labels → training signal → next-gen reviewer.

Who you'll work with

You'll be the RL person on a small, serious team tackling a frontier problem.

TokensFounder referencePortfolio piece

Helpful:PythonRL / RLHFML trainingLLMsData engineering