QA Engineer, AI Verification Systems
Confirmed on the employer's own hiring board on Nov 1, 2026. First seen by Alion on Oct 22, 2026.
Company
Pramaana Labs is a frontier AI lab building the verification layer for AI. Founded in 2025 and headquartered in Palo Alto, we turn complex human knowledge, including tax codes, legal rules, clinical guidelines and security protocols, into a formal representation, so every AI answer can be traced, challenged, and proved. Pramaana's architecture pairs foundation models trained to formalize and reason with a symbolic world model encoded in Lean 4. Pramaana was founded by a team out of Google, DeepMind, and Glean, combining frontier AI researchers and formal methods experts.
The Role
QA/SDET (Software Development Engineer in Test) for a rules-to-proof product. You test every stage of the pipeline, from rule input through formal specs, prover output and proof artifacts to the customer-facing APIs and proof viewers. You raise clear, reproducible defects and verify fixes.
Focus areas: Corner cases and security risks, including exceptions, thresholds, conflicting rules, unsatisfiable inputs, malformed or malicious input, and access and data-handling issues.
Long-term goal: Automate these tests so problems are flagged without manual intervention.
What You'll Do
Test the different parts of the product for functionality, from rule input through formal specs, prover output and proof artifacts to the customer-facing APIs and proof viewers. Raise clear, reproducible defects and verify fixes.
Cover corner cases and potential security issues, including exceptions, thresholds, conflicting rules, unsatisfiable inputs, malformed or malicious input, and access and data-handling risks.
As time goes on, work on automating these tests so that these are flagged without manual intervention.
You Will Thrive in This Role If You Have
3+ years in QA, SDET or ML/LLM evaluation, hands-on test automation experience (Python preferred)
Experience testing non-deterministic or data-heavy systems (LLMs, ML pipelines, rules engines).
Automation and CI/CD experience (pytest or similar, GitHub Actions or Jenkins).
Logical rigor: edge cases, constraints, counterexamples.
Ability to turn dense rule-based text into precise test cases, and to write clear bug reports.
Especially Strong Candidates May Also Have
LLM eval and benchmark design.
Tax, law or compliance background.
Property-based testing and API testing.