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Formal Verification Engineer - AI - Toronto, Canada

Cognichip Inc.

Toronto, Ontario, CanadaFull-timeNo compensation foundPosted 3mo agoVerified open 4 days ago

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At a glance

Compensation
No compensation found
Location
Toronto, Ontario, Canada
Schedule
Full-time
Work Authorization
Not specified

Requirements

Credentials this posting asks for.

Doctorate

Job overview

Cognichip Inc. is hiring a Formal Verification Engineer - AI - Toronto, Canada. Cognichip Inc. is seeking a Formal Verification Engineer to join its AI verification team in Toronto. The role involves mathematically proving that complex systems behave as specified, focusing on rigorous analysis and problem-solving. Candidates should possess strong analytical minds and a talent for precise reasoning, with a background in formal methods, mathematics, physics, or other quantitative disciplines.

Key focus areas include Develop and apply formal verification environments for complex systems., Translate specifications and design documents into rigorous formal properties., and Perform property checking, model checking, and automated proof development..

Successful candidates bring Bachelor's In Computer Science Or Equivalent, Demonstrated Strength In Mathematical Reasoning, and Solid Programming Skills. Important skills include Mathematical Reasoning, Programming, Python, and C++. Preferred (not required): Formal Verification, Model Checking, Property Specification, and Automated Theorem Proving.

Skills & qualifications

RequiredNice to have

Skills

Mathematical ReasoningProgrammingPythonC++Formal VerificationModel CheckingProperty SpecificationAutomated Theorem ProvingCoqLeanIsabelleACL2Z3CVC5Verification ToolingOpen-Source Formal MethodsLogicType TheoryDiscrete MathematicsMathematical Physics

Qualifications

BS, MS, or Ph.D. In Computer Science, Mathematics, Physics, or Another Rigorous Quantitative Field

Full job description

Job Title Formal Verification Engineer - AI About the Role - We are looking for exceptional analytical minds to join our verification team. Our work centers on a hard and rewarding problem: mathematically proving that complex systems behave exactly as specified — no edge cases, no exceptions.- You might be an experienced formal methods practitioner, or you might come from pure mathematics, physics, or another rigorous quantitative discipline and be looking to apply your skills to concrete, high-impact engineering problems.- If you have a talent for precise reasoning, constructing airtight arguments, and learning new technical domains quickly, we will teach you the rest. Key Responsibilities - Develop and apply formal verification environments for complex systems.- Translate specifications and design documents into rigorous formal properties.- Perform property checking, model checking, and automated proof development; debug and root-cause counterexamples.- Improve verification coverage, methodology, and reusability across projects.- Develop scripts and utilities to support verification productivity. Required Qualifications - BS, MS, or Ph.D. in Computer Science, Mathematics, Physics, or another rigorous quantitative field.- Demonstrated strength in mathematical reasoning — through formal verification experience, research, competition mathematics, or comparable evidence of rigor.- Solid programming skills (e.g., Python, C++) and the drive to rapidly master new tools and domains. Preferred Qualifications - Hands-on experience with formal verification: model checking, property specification, or automated theorem proving.- Experience with interactive theorem provers (e.g., Coq, Lean, Isabelle, ACL2) or SMT solvers (e.g., Z3, CVC5).- Experience building verification tooling or contributing to open-source formal methods projects.- Background in logic, type theory, discrete mathematics, or mathematical physics. What We Offer - The chance to work on genuinely hard correctness problems where rigor matters.- Mentorship from experienced formal methods engineers and a structured ramp-up into the field.- A culture that values depth, precision, and first-principles thinking.

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