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

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.

1) Build AI tools that planners actually use

• Develop and maintain internal tools (apps, dashboards, workflows) that operationalize advanced analytics for spares planning decision-making.
• Translate planning problems into well-scoped product requirements: user journeys, success metrics, data needs, and rollout plans.
• Create “self-serve” tools that reduce manual effort and scales insights across the organization

2) Design and implement graph databases for AI use cases

• Define the graph data model (nodes/edges, ontology/taxonomy, temporal relationships, metadata) to represent spares demand, parts, tools, configurations, sites, and operational signals.
• Build ingestion pipelines and data quality checks to keep the graph accurate, explainable, and trusted.
• Enable AI and analytics on top of the graph: graph traversals, similarity search, embeddings, and graph ML patterns that support decision tools.

3) Apply ML / AI to spares planning outcomes

• Develop predictive models for demand forecasting (including intermittent/long-tail behavior), demand drivers, and related planning signals.
• Support inventory planning improvements (e.g., safety stock, multi-echelon thinking, service-level tradeoffs) by connecting model outputs to actionable recommendations.
• Partner with SMEs to validate model behavior, define guardrails, and ensure outputs are usable and explainable in operational settings.

4) Productionize: reliability, governance, and MLOps

• Implement testing, monitoring, documentation, versioning, and performance practices so tools are robust and maintainable.
• Establish repeatable deployment patterns (dev/test/prod), model monitoring, and data lineage appropriate for enterprise planning environments.
• Create clear documentation and enablement materials so tools can be adopted broadly (not just by technical users).

What Success Looks Like (examples of outcomes)

• Planners can answer critical questions faster (and with less manual wrangling) because data and relationships are captured in a reusable graph and exposed through intuitive tools.
• Improved forecast quality and earlier detection of demand changes for targeted segments (especially long-tail/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.

Minimum Qualifications

Skills & Experience Needed

Required skills (must-have)

AI / ML & analytics

• Strong Python skills for data and ML development (pandas/numpy, ML libraries, model evaluation).
• Experience developing customer demand prediction models, or other operational decision problems.

Graph theory & graph data

• Solid foundation in graph theory concepts (graph modeling, connectivity, centrality, communities, bipartite/multipartite graphs, temporal graphs).
• Hands-on experience building with a graph database (e.g., Neo4j or similar): schema design, query patterns, performance considerations.
• Familiarity with graph embeddings and/or graph ML concepts (node/edge embeddings, message passing, link prediction, similarity).

Software & data engineering

• Strong SQL and data modeling; ability to build reliable pipelines across large enterprise datasets.
• Experience building production services or internal tools (APIs, web apps, dashboards) with a focus on usability and maintainability.

Collaboration

• Proven ability to work with non-technical stakeholders, convert ambiguous business needs into effective tools, and drive adoption/change management.

Preferred skills (nice-to-have)

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

Education & Qualifications

Minimum / typical qualification guidelines

• MS or PhD in Computer Science, Data Science, Statistics, Applied Mathematics, Operations Research, Industrial Engineering, or a related quantitative field; OR
• MS + 3+ years relevant experience; OR
• BS + 5+ years relevant experience in software engineering / ML engineering / data engineering with demonstrated delivery of production tools.

About KLA

We provide advanced inspection tools, metrology systems, process solutions, and computational analytics that make electronics possible, tackling complex challenges. From electron and photon optics to machine learning and data analytics, we seek perfection at the most fundamental level of matter in the universe. If you want to make electronics that push industries forward and make the world a better place, join us.

Total Rewards

KLA’s total rewards package for employees may also include participation in performance incentive programs and eligibility for additional benefits including but not limited to: medical, dental, vision, life, and other voluntary benefits, 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, and family care and bonding leave.

Interns are eligible for some of the benefits listed. Our pay ranges are determined by role, level, and location. The range displayed reflects the pay for this position in the primary location identified in this posting. Actual pay depends on several factors, including state minimum pay wage rates, location, job-related skills, experience, and relevant education level or training. We are committed to complying with all applicable federal and state minimum wage requirements where applicable. If applicable, your recruiter can share more about the specific pay range for your preferred location during the hiring process.

Use of AI Statement 

At KLA, our interviews seek to understand your individual skills, problem-solving approach and authentic thinking. To ensure a fair and consistent evaluation, the 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. Use of these tools may interfere with our ability to evaluate your individual qualifications and affect your candidacy. KLA is committed to advancing innovation through responsible AI, and we value candidates who share this mindset.

Equal Opportunity Statement

KLA is proud to be an Equal Opportunity Employer. We 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. Please contact us at [email protected] or at +1-408-352-2808 to request accommodation.

For additional information, view the US Know Your Rights poster on the U.S. Equal Employment Opportunity Commission website.