Interested in this AI/ML Engineer role at Cirrus Logic?
Apply Now →Skills & Technologies
About This Role
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!
### The Role
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.
### What You’ll Own \& Create
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.
### Skills You’ll Bring to the Team
- 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.
### Preferred Knowledge, Skills, and Experience
- 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.
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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cirrus Logic, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cirrus Logic AI Hiring
Cirrus Logic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.