Sr. AI/ML Engineer - Systems

KLA•Ann Arbor, MI

2d•$129,600 - $190,067•Onsite

About The Position

KLA'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. 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 a AI/ML Engineer – Systems to help turn AI/ML research into systems that run reliably in production. In this role, you’ll work with a small, collaborative team of ML engineers, subject‑matter experts, software engineers, and product partners to integrate AI/ML capabilities into scalable, production‑ready systems that teams depend on. This is a hands‑on systems role focused on integration, performance optimization, and operational excellence for AI/ML workloads. If you enjoy owning 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.

Requirements

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 processing technologies.
Demonstrated ability to lead technical projects and work effectively in matrix environments.
Ability to communicate technical concepts to non-technical team members and business partners.
Familiarity with software engineering best practices, including version control (Git), CI/CD pipelines, agile methodologies, and system design patterns.

Nice To Haves

Experience with MLOps or ML systems engineering practices such as CI/CD, monitoring, model versioning, or rollback strategies.
Familiarity with containerized or cloud‑based deployment environments.
Exposure to performance tuning, reliability engineering, or operational support for AI/ML systems.

Responsibilities

Integrate AI/ML components into existing applications, services, and pipelines, with a focus on long‑term reliability and maintainability.
Improve system and model performance across latency, throughput, reliability, and resource efficiency, and participate in practical trade‑off discussions.
Collaborate on the design and implementation of scalable, resilient AI/ML systems that evolve as new use cases emerge.
Help ensure AI/ML systems meet enterprise expectations for security, compliance, observability, and operational readiness.
Support deployment, monitoring, and troubleshooting of AI/ML systems in production.
Work closely with cross‑functional partners to deliver stable, high‑impact AI capabilities.