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# Software Engineer

- **Company:** AZX INCORPORATED
- **Location:** Seattle, WA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Test Suite, Application Programming Interfaces (APIs), Code Coverage, Computer Programming, Python (Programming Language), PostgreSQL, Regression Testing, Service Design, SPARQL, Large Language Models, Multi-Agent Systems, Fastapi, Formal Methods, Virtual Agents
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/senior-software-engineer-formal-methods-agentic-systems-azx-9819409

## About the Role

* 5+ years of productionization experience: you've taken someone else's prototype or research code to production, with packaging, tests, CI, observability, and docs, respecting the design you inherited while changing it with evidence.
 * Real depth in formal methods - you've built things with SMT/constraint solvers (Z3-class), automated theorem proving, or heuristic search over proof and plan spaces (AO*-class), and can speak to soundness, completeness, and their practical costs.
 * Experience with data modeling and shape validation - ontology/taxonomy design, SHACL shapes as data contracts, RDF/OWL/SPARQL, or comparable schema-level validation on knowledge graphs - where you've modeled domains, not just queried them.
 * Agentic AI literacy - you've built or wired LLM agents, understand their failure modes, and know exactly why an agent may propose but never assert around formal tooling.
 * Strong generalist engineering skills: Python fluency, service design, and the judgment to keep a powerful system simple to use.
 * Comfort partnering closely with a principal engineer/inventor - direct, kind candor, with no ego about whose idea wins.
 * Practical familiarity with our core stack - Z3/SMT/SAT solvers, constraint programming, SHACL/RDF/OWL/SPARQL, Python 3.12+ (type-strict, FastAPI when needed), and Postgres.
 * Experience with test engineering for formal systems - counterexample regression testing, property-based testing - and CI/release discipline for libraries.
 * Working knowledge of LLM provider APIs and agent frameworks (or hand-rolled agent loops), even if your primary depth is on the formal-methods side.
 * Bachelor's Degree: Master's is a Plus
 * Domain experience in Energy, Utilities, Commercial Real-Estate, and Infrastructure is a plus

## Description

We are seeking a Software Engineer to help build and implement models, using agentic problem-solving wrapped around formal methods, that solve client issues that have strict compliance rules, tariffs, equipment constraints and much more . You'll partner directly with the system's inventor, learn the design deeply, and take it to production - packaging, testing, CI, documentation, and the interface that lets every AZX engineer put provably-right answers into client solutions. This is the formal solutioning layer of the stack: the part that produces checked answers when a client's problem actually has one. You'll sit between research and production, comfortable in both, and help shape where the system goes next., * Take the research-grade formal/agentic system to production: a real package, test suite, service interface, documentation a cold-joiner can use, and a release cadence.

 * Own the architecture for reliability, packaging, test coverage, typing, CI, performance, and release discipline of the formal/agentic system.
 * Wrap solver runs in agent loops where the agent proposes and the solver disposes, deliberately defining what the agent may touch when a proof fails.
 * Model messy client business rules - compliance requirements, rate structures, program eligibility, design constraints - as constraints and shapes that check mechanically, and build the review habit that keeps those models honest.
 * Reason over per-customer digital twins, checking proposed changes against the twin's constraints and shapes before anyone touches the real system.
 * Define the agent seam: where LLM agents may assist (translation, hypothesis, explanation) and where they're forbidden (anything that asserts).
 * Make proof results legible to client stakeholders who will never read a proof - clearly communicating what was checked, against what, and what was not checked.


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