
Sr. AI/ML Engineer Systems
Ann Arbor, MIJobSeen 5 days agoSeen in employer's feed 5 days ago
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Job overview
KLA’s IT group supports business growth and productivity through technology, analytics, and process excellence. The AI/ML Engineer – Systems will help turn AI/ML research into reliable production systems, integrating capabilities into scalable applications and pipelines. The role is based at KLA’s Midwest headquarters in Ann Arbor and involves performance optimization, operational support, and collaboration with engineering and product partners.
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Full job description
To make electronics, you need chips, wafers, transistors, reticles, and... To make these, you must see, test and manufacture them at scale-faster and better than ever before. That's where KLA comes in. Whether you're early in your career or an experienced professional, you'll solve complex challenges, work alongside brilliant minds and help shape the future of technology. Group/DivisionKLA's IT group supports business growth and productivity by connecting people, process and technology around the world. We work to improve the technology that drives our business to thrive and focus on empowering employee use of technology. This integrated approach to customer service, creativity and technological excellence enhances employee productivity, business analytics and process excellence. What You'll Do In this role, you will play a key part in advancing business priorities by delivering high-impact work across your area of expertise. We are seeking aAI/ML Engineer - Systemsto helpturn AI/ML research into systems that run reliably in production. In this role, you'll work with asmall, collaborative teamof ML engineers, subjectmatter experts, software engineers, and product partners to integrate AI/ML capabilities intoscalable, productionready systems that teams depend on. This is ahandson systems rolefocused on integration, performance optimization, and operational excellence for AI/ML workloads. If you enjoyowning real problems, learning how complex systems scale, and improving AI systems that are actively used in production, we'd love to hear from you! This role will be based at our Midwest HQ in Ann Arbor, MI. Key Responsibilities Integrate AI/ML components intoexisting applications, services, and pipelines, with a focus on longterm reliability and maintainability. Improve system and model performance acrosslatency, throughput, reliability, and resource efficiency, and participate in practical tradeoff discussions. Collaborate on the design and implementation ofscalable, resilient AI/ML systemsthat evolve as new use cases emerge. Help ensure AI/ML systems meetenterprise expectationsfor security, compliance, observability, and operational readiness. Support deployment, monitoring, and troubleshooting of AI/ML systems in production. Work closely with crossfunctional partners to deliverstable, highimpact AI capabilities. Preferred Qualifications Experience withMLOps or ML systems engineeringpractices such as CI/CD, monitoring, model versioning, or rollback strategies. Familiarity with containerized or cloudbased deployment environments. Exposure to performance tuning, reliability engineering, or operational support for AI/ML systems. Why You'll Enjoy Working With Us We build AI systems the same way we build teams-byworking closely together, learning continuously, and being thoughtful about the tradeoffs we make. You'll deepen your systems expertise by working on AI solutions used by real teams, seeing how those systems behave in production, and learning from experienced engineers along the way. As you grow, you'll also have opportunities totake on more ownership and mentor others, shaping how AI systems are built and operated. Minimum Qualifications Bachelor's degree in Computer Science, Data Science, AI, or a related field. Master's and/or PhD preferred Minimum of eight (8) years of relevant software engineering, along with 3-5 years of experience in AI/ML, with a proven track record of deploying solutions in a production environment. Proficiency in Python, or similar languages, with experience in libraries like TensorFlow, Scikit-learn, etc. Experience with RAG is essential. Strong background in statistics, machine learning algorithms, deep learning, neural networks, and system architecture. Strong understanding of cloud platforms, DevOps practices, and data proces
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