Formal Verification - AI/ML Engineer

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, LangChain, LlamaIndex, 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, SystemVerilog, 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.