Grafton Sciences

Grafton Sciences is focused on building physical superintelligence to accelerate scientific discovery. Their mission is to create systems capable of autonomous scientific discovery, utilizing tools that expand our capabilities. They are developing an at-home early detection platform for cancer, leveraging advances in synthetic biology and nanotechnology. With a $42.5 million contract from ARPA-H, Grafton aims to detect over 48 tumors at Stage I, representing a significant portion of the global cancer burden. Their approach integrates computational modeling, automated experimentation, and materials engineering, positioning them as leaders in the field of disease detection and scientific innovation.

AIAutonomous SystemsCancer DetectionHealth TechnologyMaterials EngineeringReinforcement LearningScientific DiscoverySuperintelligencegraftonsciences.com

Grafton Sciences

AIAutonomous SystemsCancer DetectionHealth TechnologyMaterials EngineeringReinforcement LearningScientific DiscoverySuperintelligencegraftonsciences.com

HQRedwood City, US

Team Size5

Open Jobs30

Total Funding-

Latest FundraiseUnknown

Join the Team

Ontology Engineer — Formal Verification & Automated Reasoning

On-SiteSan Francisco Bay Area, US

On-Site • San Francisco Bay Area, US

Teeming tracks opportunities at over 24,000 AI startups, then works with you to find (and land) the one you'll love.

Technical Writer

Part-timeUtrecht, NL

Part-time • Utrecht, NL

DevOps Engineer

ContractBelgrade, RS

Contract • Belgrade, RS

Mobile Developer

Full-timeMunich, DE

Full-time • Munich, DE

Data Scientist

Part-timeNiš, RS

Part-time • Niš, RS

Software Engineer

Part-timeAustin, US

Part-time • Austin, US

DevOps Engineer

Full-timeNiš, RS

Full-time • Niš, RS

About the Role

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.

What You’ll Do

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.

What We’re Looking For

Ontology & Semantics

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.

Formal methods

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)

Engineering depth

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.

Nice to Have

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.

Education

MS or PhD in Computer Science, Information Science, Mathematics, or a related field
(or equivalent depth through experience).

Above all, we look for candidates who can demonstrate world-class excellence.

Compensation

We offer competitive salary, meaningful equity, and benefits.