Service Supply Chain AI Engineer

KLA Services is seeking a Service Supply Chain AI Engineer to design and productionize AI/ML and graph-based tooling that supports spares planning decisions. The role emphasizes predictive demand forecasting, graph database modeling and implementation, and MLOps practices to help planners and operational stakeholders use insights in day-to-day workflows.

Responsibilities

• Develop and maintain internal tools such as applications, dashboards, and workflows that operationalize advanced analytics for spares planning decision-making.
• Convert planning challenges into well-scoped product requirements, including user journeys, success metrics, data needs, and rollout plans.
• Create self-serve tools to reduce manual work and scale insights across the organization.
• Define the graph data model, including nodes and edges, ontology or taxonomy, temporal relationships, and metadata representing spares demand, parts, tools, configurations, sites, and operational signals.
• Build ingestion pipelines and data quality checks to support an accurate, explainable, and trusted graph.
• Enable AI and analytics on top of the graph, including graph traversals, similarity search, embeddings, and graph ML patterns for decision tools.
• Develop predictive models for demand forecasting, including intermittent and long-tail behaviors, along with demand drivers and related planning signals.
• Support inventory planning improvements by connecting model outputs to actionable recommendations, including safety stock, multi-echelon thinking, and service-level tradeoffs.
• Collaborate with SMEs to validate model behavior, establish guardrails, and ensure outputs remain usable and explainable in operational settings.
• Implement testing, monitoring, documentation, versioning, and performance practices to keep tools robust and maintainable.
• Establish repeatable deployment patterns across dev/test/prod, including model monitoring and data lineage for enterprise planning environments.
• Create clear documentation and enablement materials so tools can be adopted broadly beyond technical users.

Required Qualifications

• Strong Python skills for data and ML development, including pandas/numpy, ML libraries, and model evaluation.
• Experience developing customer demand prediction models or other operational decision problems.
• Solid foundation in graph theory concepts, including graph modeling, connectivity, centrality, communities, and bipartite or multipartite graphs, including temporal graphs.
• Hands-on experience building with a graph database such as Neo4j or similar, including schema design, query patterns, and performance considerations.
• Familiarity with graph embeddings and/or graph ML concepts such as node/edge embeddings, message passing, link prediction, and similarity.
• Strong SQL skills and data modeling ability to build reliable pipelines across large enterprise datasets.
• Experience building production services or internal tools (APIs, web apps, dashboards) with an emphasis on usability and maintainability.
• Demonstrated ability to partner with non-technical stakeholders, convert ambiguous business needs into effective tools, and drive adoption and change management.
• Education and experience alignment: MS or PhD in Computer Science, Data Science, Statistics, Applied Mathematics, Operations Research, Industrial Engineering, or a related quantitative field; OR MS with 3+ years relevant experience; OR BS with 5+ years relevant experience in software engineering/ML engineering/data engineering with demonstrated delivery of production tools.

Technologies

• Python
• pandas
• numpy
• SQL
• Neo4j
• APIs
• Web apps
• Dashboards
• Embeddings

Preferred Skills (Nice to Have)

• Supply chain planning experience, including service parts, inventory optimization, safety stock, service-level tradeoffs, and replenishment or network concepts.
• Experience with probabilistic forecasting methods and intermittent-demand approaches.
• Knowledge graphs and ontology design, including entity resolution and semantic modeling patterns.
• Experience integrating LLMs with structured data using RAG patterns, tool calling, and natural-language-to-query workflows where governance is required.
• MLOps and platform experience, including model tracking, CI/CD, monitoring, containers, and scalable compute.

Benefits

• Base Pay Range: $90,400.00 - $132,600.00 Annually
• Participation in performance incentive programs
• Medical
• Dental
• Vision
• Life
• 401(K) including company matching
• Employee stock purchase program (ESPP)
• Student debt assistance
• Tuition reimbursement program
• Development and career growth opportunities and programs
• Financial planning benefits
• Wellness benefits including an employee assistance program (EAP)
• Paid time off and paid company holidays
• Family care and bonding leave
• Paid time off (interns are eligible for some of the benefits listed)

Team / Division Context

The KLA Services team includes Service Sales, Marketing, Spares Supply Chain Management, Field Operations, Engineering, Product Training, Digital Solutions and Analytics, and Technical Product Support.

What Success Looks Like

• Planners can answer critical questions faster with less manual data wrangling due to reusable graph representations and intuitive tools.
• Improved forecast quality and earlier detection of demand changes for targeted segments, especially long-tail or intermittent parts, leading to fewer expedites and fewer stockouts.
• Reduced avoidable inventory buffers through better segmentation, variability modeling, and decision support tied directly to planning actions.

AI Use Statement

Use of AI, recording tools, or other technologies to generate, suggest, or provide responses during interviews, whether virtual or in person, is not permitted unless explicitly approved in advance as part of a reasonable accommodation or invited by the interviewer.

Equal Opportunity Statement

KLA is proud to be an Equal Opportunity Employer and will ensure that qualified individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment.