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Software Engineering Manager-AI​/ML

Job in Ann Arbor, Washtenaw County, Michigan, 48113, USA
Listing for: KLA
Full Time position
Listed on 2026-10-19
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 114100 - 194000 USD Yearly USD 114100.00 194000.00 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 looking for a Software Engineering Manager to lead and manage a team of software engineers focused on developing and deploying AI/ML solutions for KLA's advanced inspection and metrology products. This role combines technical leadership, team management, and strategic planning to drive innovation and enhance product capabilities through artificial intelligence and machine learning.

Key Responsibilities
  • Lead and mentor a team of software engineers, fostering a culture of innovation, collaboration, and continuous improvement
  • Define, plan, and execute AI/ML software development projects from conception to deployment, ensuring adherence to schedules, budgets, and quality standards
  • Provide architectural and design guidance for: ML pipelines and model lifecycle management, LLM applications (e.g., RAG, agents, copilots, tool/function calling and integration of AI services into enterprise applications)
  • Review designs and key technical decisions; ensure alignment with platform standards and best practices
  • Partner with data, cloud, and platform teams to ensure AI solutions align with enterprise architecture
  • Collaborate with security, legal, and governance teams to ensure responsible AI, privacy, and compliance requirements are met
  • Communicate status, risks, and outcomes clearly to leadership and stakeholders
  • Ensure AI systems meet standards for reliability, monitoring, cost management, and incident response
  • Establish guardrails for responsible AI: data privacy, model risk, transparency, bias awareness, and auditability
  • Drive continuous improvement through retrospectives, metrics, and adoption of best practices.
Preferred / Nice to Have
  • Managing teams building LLM‑based applications or GenAI platforms
  • Familiarity with cloud platforms (e.g., Azure, AWS, or GCP) and modern data stacks
  • Exposure to MLOps practices, model monitoring, and AI governance frameworks
  • Experience delivering AI solutions in enterprise domains such as manufacturing, supply chain, finance, or customer operations
Core Competencies
  • AI Delivery: ML lifecycle, LLM applications, production readiness
  • Technical Judgment: architecture reviews, trade‑off decisions
  • Execution: roadmap planning, dependency management, risk mitigation
  • Collaboration:

    product partnership, stakeholder communication
  • Responsible AI: security, privacy, governance awareness
  • People Leadership: coaching, feedback, performance management, hiring
Minimum Qualifications
  • Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience)
  • 10+ years of experience in software engineering, ML/AI engineering, or applied data roles
  • 4+ years of people management experience leading technical teams
  • Strong understanding of AI/ML systems and how they are built, deployed, and operated in production
  • Hands‑on background with modern engineering…
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