Job description

For over four decades, Cirrus Logic has been propelled by the top engineers in mixed-signal processing. Our rockstar team thrives on solving complex challenges with innovative end-user solutions for the world's top consumer brands. Cirrus Logic is also known for its award-winning culture, which was built on a foundation of inclusion and fairness, meaningful community engagement, and delivering enjoyable employee experiences at every turn. But we couldn’t do it without our extraordinary workforce – and that’s where you come in. Join our team and help us continue to make Cirrus Logic an exceptional place to grow your career! Our Customer Enablement organization defines semiconductor system requirements, develops technical documentation, and supports internal teams and customers in using our devices. We are seeking an Applied AI/Automation Engineer to design, build, deploy, and support practical AI solutions that improve internal engineering workflows, automate repetitive tasks, and make technical information easier to find and use. This role will apply the latest AI techniques within scalable software solutions, helping move promising ideas from experimentation into reliable, maintainable tools that deliver measurable value. The role will involve designing solutions that are secure, measurable, maintainable, and useful in people’s daily work. This role will also contribute to the development of documentation and information tools, scripts, and utilities. We eagerly pursue scripting, data modeling, and process automation to operate more efficiently in collaboration with work partner teams. The ideal candidate combines strong software engineering skills with hands-on experience building AI-enabled applications. Depending on business priorities, initiatives may include: Building LLM-powered assistants and copilots Creating AI agents and tool-integrated workflows that connect to enterprise systems Automating repetitive engineering and operational tasks Applying document intelligence, extraction, classification, and summarization Developing internal developer-productivity tools Designing evaluation methods, quality metrics, monitoring, and feedback loops Improving prompts, workflows, context retrieval, model selection, performance, and cost Establishing secure deployment patterns, access controls, and responsible AI practices Success in this role means delivering AI capabilities that are useful, trusted, adopted, and sustainable in production while helping engineering teams use AI efficiently and effectively. Key Responsibilities Design, develop, and implement new tools, scripts and utilities to help other engineering teams operate more efficiently Write clean, well-documented code with a focus on scalability, performance, and maintainability Conduct unit and integration testing to ensure code quality and stability Identify and prioritize high-value AI use cases aligned with business and engineering needs Design, build, test, and deploy AI-enabled skills, agents and workflows Integrate AI capabilities with internal data sources, developer tools, documentation platforms, and enterprise systems Create evaluation methods to measure output quality, reliability, accuracy, usage, and user value Partners with Applied AI and Security to ensureImplement safeguards for security, privacy, access control, and responsible AI usage Document how to use the tools, as well as architecture, design decisions, implementation patterns, and operational practices Stay current on the evolving AI tool and model landscape and recommend pragmatic adoption strategies Required Knowledge, Skills and Abilities Bachelor’s degree in Computer Science, Software Engineering, Electrical Engineering, or related field, or equivalent practical experience 6+ years of professional software engineering experience, including building and deploying production software Strong programming skills in one or more languages such as Python, JavaScript, TypeScript, Java, or C# Comfortable working with source control (Git, SVN) Hands-on experience designing and building AI-enabled applications using large language model APIs, AI application frameworks, enterprise AI platforms, or similar technologies Experience integrating software with APIs, cloud services, enterprise data sources, data pipelines, and knowledge repositories Experience with AI evaluation, measurement, monitoring, observability, or feedback practices Working knowledge of AI governance, responsible AI, cybersecurity, data privacy, and access-control considerations Strong written and verbal communication skills, with experience working directly with internal customers and cross-functional teams Strong problem-solving skills, including the ability to define abstract problems, prioritize competing needs, and develop concise, actionable solutions in ambiguous and fast-moving environments Prior experience in the semiconductor or high-tech industry Preferred Knowledge, Skills and Abilities Demonstrated ability to move AI prototypes into production, including testing, deployment, monitoring, maintenance, and retirement of solutions that no longer provide value Experience designing AI agents, multi-step workflows, tool integrations, human-in-the-loop controls, or agent security patterns Experience building reusable AI frameworks, platform components, skills, or developer-enablement resources that help other engineers adopt AI Experience optimizing AI systems for quality, latency, reliability, and cost through techniques such as model selection, routing, caching, batching, or context management Experience implementing enterprise AI security practices, including service identity, scoped permissions, secrets management, audit-ability, data residency, or protection of confidential intellectual property Experience with rigorous AI experimentation and evaluation, including test-set design, sampling, human quality review, inter-rater reliability, or measurement of user and business outcomes Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time. Export control restrictions based upon applicable laws and regulations would prohibit candidates who are nationals of certain embargoed countries from working in this position without Cirrus Logic first obtaining an export license. Candidates for this role must be able to access technical data without a requirement for an export license. We are unable to sponsor or obtain export licenses for this role. Cirrus Logic strives to select the best qualified applicant for any opening. Different approaches, ideas and points of view are both valued and respected. Employment decisions are made on the basis of job-related criteria without regard to race, color, religion, sex, national origin, age, protected veteran or disabled status, genetic information, or any other classification protected by applicable law.

Requirements

Must-haves

Our Customer Enablement organization defines semiconductor system requirements, develops technical documentation, and supports internal teams and customers in using our devices.
The ideal candidate combines strong software engineering skills with hands-on experience building AI-enabled applications.
Depending on business priorities, initiatives may include:
Building LLM-powered assistants and copilots
Creating AI agents and tool-integrated workflows that connect to enterprise systems
Automating repetitive engineering and operational tasks
Applying document intelligence, extraction, classification, and summarization
Developing internal developer-productivity tools
Designing evaluation methods, quality metrics, monitoring, and feedback loops
Improving prompts, workflows, context retrieval, model selection, performance, and cost
Establishing secure deployment patterns, access controls, and responsible AI practices
Success in this role means delivering AI capabilities that are useful, trusted, adopted, and sustainable in production while helping engineering teams use AI efficiently and effectively.
Design, develop, and implement new tools, scripts and utilities to help other engineering teams operate more efficiently
Write clean, well-documented code with a focus on scalability, performance, and maintainability
Conduct unit and integration testing to ensure code quality and stability
Identify and prioritize high-value AI use cases aligned with business and engineering needs
Design, build, test, and deploy AI-enabled skills, agents and workflows
Integrate AI capabilities with internal data sources, developer tools, documentation platforms, and enterprise systems
Create evaluation methods to measure output quality, reliability, accuracy, usage, and user value
Partners with Applied AI and Security to ensureImplement safeguards for security, privacy, access control, and responsible AI usage
Document how to use the tools, as well as architecture, design decisions, implementation patterns, and operational practices
Stay current on the evolving AI tool and model landscape and recommend pragmatic adoption strategies
Required Knowledge, Skills and Abilities

Nice-to-haves

Preferred Knowledge, Skills and Abilities

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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Platform: lever
Effort: quick