Ontology Engineer — Formal Verification & Automated Reasoning

Grafton Sciences•San Francisco, CA

About The Position

We are seeking an Ontology Engineer specializing in Formal Verification and Automated Reasoning to define and maintain the formal semantic foundations of complex software and AI-driven systems. In this role, ontologies are not documentation—they are executable specifications that define domain meaning, invariants, and constraints. You will design ontological models that serve as the source of truth for formal specifications, and work with verification and reasoning tools to ensure systems behave correctly with respect to those semantics. This role sits at the intersection of ontology engineering, logic and system correctness.

Requirements

Strong experience in ontology engineering or knowledge representation.
Ability to define precise semantics for complex, evolving domains.
Experience with ontological constraints, typing systems, and validation rules.
Hands-on experience with formal verification or automated reasoning.
Familiarity with one or more of: SMT solvers (Z3, CVC5) Model checkers (TLA+, Alloy) Theorem provers (Coq, Lean, Isabelle) Rule-based or logic programming systems (Datalog, Prolog)
Strong programming skills (e.g., Python, Rust, OCaml, Java).
Experience building or integrating semantic tooling into production systems.
Ability to balance formal rigor with engineering practicality.
MS or PhD in Computer Science, Information Science, Mathematics, or a related field (or equivalent depth through experience).

Nice To Haves

Experience compiling OWL/RDF ontologies into logical constraints.
SHACL or constraint-based validation tied to verification pipelines.
Background in type theory, semantics, or programming languages.
Experience verifying AI, agentic, or neuro-symbolic systems.
Contributions to ontology standards, verification tools, or open-source semantic systems.

Responsibilities

Design and maintain formal ontologies capturing domain concepts, relations, and invariants.
Encode ontological constraints into machine-checkable specifications used by verification tools.
Translate ontologies into logical representations suitable for SMT solving, model checking, or theorem proving.
Define and validate semantic invariants that systems must preserve across all executions.
Collaborate with software, systems, and AI engineers to align implementations with formal semantic contracts.
Develop tooling and pipelines that connect ontologies to verification and reasoning workflows.
Analyze counterexamples and verification failures to refine ontologies and system designs.
Support reasoning systems, planners, or agents by ensuring their inputs and outputs are ontologically valid.