AI Engineer

MaxLinear, Inc.•Irvine, CA

16h•$75,000 - $102,000

About The Position

MaxLinear is seeking a highly motivated Artificial Intelligence Engineer to evaluate and integrate cutting-edge AI technologies into the semiconductor engineering workflows. This role sits at the intersection of AI innovation, engineering productivity, and enterprise-scale deployment, helping shape how modern AI systems are operationalized across teams. You will play a critical role in identifying high-impact use cases, benchmarking AI platforms, and delivering scalable, secure solutions that drive measurable business value. In this role, you will focus on the following: Evaluate AI platforms and large language models (LLMs), including GPT, Claude, Gemini, and SWE-class systems Contribute to the design and execution of benchmarks to measure quality of results (QoR), productivity gains, and scalability Identify strengths, limitations, and optimal use cases across AI tools and architectures Partner with cross-functional teams to deploy solutions across multiple domains Validate and implement AI-driven solutions within real-world semiconductor engineering workflows Partner with engineering to translate engineering use cases into production-ready AI capabilities Partner with internal teams to develop and analyze ROI and cost-performance tradeoffs across cloud and on-prem deployment models Provide data-driven recommendations on AI platform selection and scaling strategies Optimize resource utilization for maximum business impact Design and support LLM deployments across hybrid environments (cloud and on-prem) Integrate with enterprise data platforms, including data lakes and MCP gateway architectures Ensure performance, reliability, and scalability of deployed AI systems Implement best practices based on NIST and OWASP frameworks Define AI usage guardrails, governance models, and compliance policies Ensure enterprise-grade security and responsible AI adoption

Requirements

Experience working with large language models (LLMs) and AI/ML systems
Strong analytical skills with experience in benchmarking, evaluation, or performance analysis
Experience with cloud platforms (AWS, Azure, GCP) and/or on-prem infrastructure
Ability to translate technical capabilities into business value
Experience integrating AI into engineering or semiconductor workflows
Familiarity with data platforms, pipelines, and large-scale data processing
Knowledge of AI security, governance, and compliance frameworks
Hands-on experience with model deployment, APIs, and system integration
BS in Electrical Engineering or related

Responsibilities

Evaluate AI platforms and large language models (LLMs), including GPT, Claude, Gemini, and SWE-class systems
Contribute to the design and execution of benchmarks to measure quality of results (QoR), productivity gains, and scalability
Identify strengths, limitations, and optimal use cases across AI tools and architectures
Partner with cross-functional teams to deploy solutions across multiple domains
Validate and implement AI-driven solutions within real-world semiconductor engineering workflows
Partner with engineering to translate engineering use cases into production-ready AI capabilities
Partner with internal teams to develop and analyze ROI and cost-performance tradeoffs across cloud and on-prem deployment models
Provide data-driven recommendations on AI platform selection and scaling strategies
Optimize resource utilization for maximum business impact
Design and support LLM deployments across hybrid environments (cloud and on-prem)
Integrate with enterprise data platforms, including data lakes and MCP gateway architectures
Ensure performance, reliability, and scalability of deployed AI systems
Implement best practices based on NIST and OWASP frameworks
Define AI usage guardrails, governance models, and compliance policies
Ensure enterprise-grade security and responsible AI adoption