Senior Software Engineer (Formal Methods & Agentic Systems)

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.

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.

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  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.

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  Own the architecture for reliability, packaging, test coverage, typing, CI, performance, and release discipline of the formal/agentic system.

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  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.

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  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.

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  Reason over per-customer digital twins, checking proposed changes against the twin's constraints and shapes before anyone touches the real system.

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  Define the agent seam: where LLM agents may assist (translation, hypothesis, explanation) and where they're forbidden (anything that asserts).

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  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.

• 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.

• 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

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

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!