We are seeking a hands-on Senior AI Engineer to join Cirrus Logic’s centralized AI Core team. You will develop, prototype, validate, and scale AI-enabled engineering capabilities across R&D’s Hardware, Software & Firmware design teams.

This is a builder role for an engineer who can translate rapidly evolving AI technology into secure, reliable, and measurable improvements in how products are designed, tested, debugged, and maintained. In this role you will be combining modern AI and agentic workflows with Cirrus-specific product knowledge, codebases, engineering processes, test infrastructure, and real hardware.

This role is ideal for an engineer energized by solving ambiguous technical problems, validating solutions rigorously, and turning pilots into practical engineering leverage at scale.

This is not an AI demonstration role — this is where you build trustworthy engineering capabilities, establish effective validation methods, and help shape how R&D applies AI.

You will:

• Partner with Hardware, Software & Firmware leaders, architects, developers, test engineers, and DevOps to identify, prioritize, and rapidly prototype high-value AI use cases.
• Build and evaluate AI-assisted workflows for firmware and driver development, code understanding, debugging, test creation, documentation, and engineering productivity.
• Design and implement retrieval-augmented generation, contextual knowledge, tool integration, and agentic workflows that safely use approved internal engineering information.
• Connect AI systems to appropriate engineering tools, repositories, build and test systems, documentation, and, where appropriate, hardware and lab environments.
• Establish rigorous evaluation methods for AI-enabled workflows, including quality, correctness, security, developer experience, productivity, and hardware-in-the-loop validation.
• Convert successful pilots into reusable AI Core capabilities, including reference architectures, libraries, integration patterns, governance controls, documentation, and enablement materials.
• Help define the technical roadmap for AI in R&D, including build-versus-buy recommendations, vendor evaluation, data and access requirements, and deployment patterns.
• Work with security, IT, legal, and engineering leadership to ensure responsible use of models, code, confidential information, and internal tools.
• Share learnings across the AI Core team and R&D through demonstrations, technical guidance, and practical training that supports adoption.

You will not simply introduce AI tools — you will establish repeatable, validated ways for Cirrus engineers to use AI effectively in hardware-adjacent development environments.

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related discipline, with 5+ years of relevant experience in software, AI/ML systems, developer infrastructure, embedded systems, firmware, or a closely related field. Advanced degree or equivalent practical experience is valued.
• Strong Python software engineering skills, with experience delivering production-quality tools, services, or developer infrastructure.
• Practical experience applying modern AI/ML technologies, including large language models, retrieval-augmented generation, embeddings and vector search, tool calling, agents, model evaluation, or AI application platforms.
• Experience designing reliable software systems with clear interfaces, observability, testing, security, and maintainability.
• Demonstrated ability to move from an ambiguous problem statement through prototype, evaluation, and engineering adoption.
• Ability to work effectively across software, firmware, hardware, test, DevOps, IT, and security disciplines.
• Strong technical judgment, structured problem solving, and communication skills, including the ability to explain complex AI tradeoffs to technical and leadership audiences.

• Experience with embedded software, firmware, device drivers, RTOS or bare-metal development, hardware bring-up, or hardware/software co-debug.
• Experience integrating AI systems with source control, CI/CD, issue tracking, documentation systems, build systems, test automation, or laboratory equipment.
• Experience with hardware-in-the-loop, simulation, emulation, or automated validation environments.
• Knowledge of C/C++, Linux, developer tooling, code analysis, and secure software-development practices.
• Experience evaluating and deploying commercial AI products as well as building targeted custom extensions.
• Familiarity with enterprise data governance, access controls, model safety, and intellectual-property considerations.
• Experience developing reusable technical platforms, standards, or enablement materials for a broad engineering organization.