Formal Verification - AI​/ML Engineer

** Role Number:*
* ** Summary*
* Apple's Hardware Technologies Formal Verification team is seeking an AI/ML Engineer to work at the intersection of Artificial Intelligence and Formal Verification. In this role, you will explore, prototype, and build AI-powered systems - with a focus on Large Language Models - to augment and transform how formal verification is performed on Apple Silicon.
You will work closely with formal verification engineers, design engineers, and EDA tool developers to identify high-impact opportunities and deliver practical, domain-specific AI applications.

** Description*
* You will be responsible for:
Building domain-specific AI applications that leverage LLMs and other ML techniques to accelerate formal verification workflows - from specification interpretation to property generation, proof debugging, and beyond.
Developing and fine-tuning LLM-based systems tailored to hardware verification tasks, including retrieval-augmented generation (RAG) pipelines, agentic tool-use frameworks, and domain-adapted models.
Collaborating with formal verification engineers to deeply understand FV methodologies, pain points, and opportunities where AI can meaningfully improve productivity, quality, and coverage.
Prototyping novel AI-driven approaches for tasks such as automatic SVA property synthesis, natural-language-to-formal-specification translation, proof strategy recommendation, and intelligent counterexample analysis.
Evaluating and integrating emerging AI/ML research into practical, production-quality tools and workflows used by the FV team.
Establishing best practices and infrastructure for AI application development within the FV organization.

** Minimum Qualifications*
* + A minimum of a bachelor's degree in relevant field and a minimum of 10 years of relevant industry experience.

** Preferred Qualifications*
* + Strong hands-on experience building AI/ML applications, particularly those leveraging Large Language Models (LLMs) - including prompt engineering, fine-tuning, RAG architectures, agentic systems, or LLM-based tool chains.

+ Demonstrated ability to take AI capabilities from prototype to production - you have shipped or deployed AI-powered tools or applications, not just trained models.

+ Proficiency in Python and modern ML/AI frameworks and tooling (e.g., PyTorch, Lang Chain, Llama Index, Hugging Face, or similar).

+ Background in formal methods, mathematical logic, or a strong mathematical foundation - whether through academic training (e.g., formal methods, type theory, automated reasoning, mathematical logic) or applied experience. You don't need to be an FV expert, but a quantitative and rigorous mindset is essential.

+ Genuine interest in domain-specific AI applications - you are excited about going deep into a specialized engineering domain rather than building general-purpose AI products.

+ Software engineering best practices - version control, testing, API design, and building maintainable, collaborative codebases.

+ Excellent communication and interpersonal skills - you will work across disciplines with FV engineers, design engineers, and tooling teams.

+ Self-directed and comfortable with ambiguity - you will need to identify opportunities, propose solutions, and drive them forward.

+ Experience working on or contributing to LLM tooling, frameworks, or infrastructure (e.g., inference engines, model serving, evaluation harnesses).

+ Prior exposure to hardware design or verification concepts (RTL, System Verilog, assertions, EDA tools).

+ Familiarity with formal methods, SAT/SMT solvers, model checking, or theorem proving.

+

Experience with code generation or analysis tasks using LLMs.

+ MS or PhD in Computer Science, Electrical Engineering, Mathematics, or a related field - though exceptional industry experience is equally valued.