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Sr. AI​/ML Engineer - Systems

Job in Ann Arbor, Washtenaw County, Michigan, 48113, USA
Listing for: Aimlroles
Full Time position
Listed on 2026-10-09
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 129600 - 190067 USD Yearly USD 129600.00 190067.00 YEAR
Job Description & How to Apply Below

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/Division

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.

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 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.

Key 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.
Preferred Qualifications
  • 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.
Why You’ll Enjoy Working With Us

We build AI systems the same way we build teams—by
working closely together, learning continuously, and being thoughtful about the trade‑offs 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 to
take 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 Tensor Flow, Scikit-learn, etc. Experience with RAG is essential.
  • Strong background in statistics, machine learning algorithms, deep learning, neural networks, and system architecture.
  • Strong…
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