About AZX

Our mission is to accelerate positive impact in critical industries through AI transformation. We specialize in physics-informed ML and enterprise AI solutions that directly address climate and sustainability challenges.

We’re growing quickly and already work with category-leaders in real estate (CBRE), energy (LevelTen Energy), logistics (Flexe) and utilities.

We’re a public benefit corporation, founded in 2024, and have been profitable since inception.

We work on challenges in clean energy, decarbonization, climate risk, energy systems, and global economics. We’re building our company for long-term success and aim to create the ultimate place to work for those passionate about AI and making a positive impact.

About This Role

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.

Responsibilities:

• 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.

Core Qualifications:

• 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

Why AZX!

• Be part of a fast-growing, profitable, mission-driven company with industry-leading clients tackling the massive opportunity of AI transformation in critical industries.

• Competitive early-stage startup compensation (based on capabilities, experience, and location)

• Bonus eligibility

• Health insurance with meaningful coverage for dependents

• Flexible paid time off

• Equity

• Fully remote culture with a cluster of teammates in Seattle

Additional Information:

• Must be able to travel 2x/year for company summits

• Applicants must be currently authorized to work in the United States on a full-time basis.

• We are unable to sponsor or take over sponsorship of employment visas at this time.

• Please note that our interview process includes a written take-home assignment followed by a live two-hour technical session with our engineering team, so if that format isn't a good fit, we'd ask that you not apply

• Please only apply to a maximum of 2 roles at a time, any applicants who apply to more then 2 roles within a 6 month period will automatically be disqualified

Next Steps:

If this job sounds like a great fit but you don’t check ALL of these qualification boxes, we’d still love to hear from you!