AI Engineer

Applied Materials
Singapore
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningSkills: ["Communication","Stakeholder management","Problem-solving","Mentoring","Cross-functional collaboration"]

Build and scale manufacturing data ecosystems by designing pipelines and data architectures that ingest, consolidate, and enable real-time analytics. Lead advanced ML and statistical modeling for problems like yield optimization and predictive maintenance. Create agentic, autonomous automation workflows for feature engineering, model execution, and decision-making at scale, partnering with manufacturing, supply chain, and engineering teams to deliver measurable operational outcomes. Communicate insights to senior leadership and help mentor multi-disciplinary teams.

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FursaFursa
Applied Materials
Applied Materials
2 days ago

AI Engineer

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Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 31 minutes agoStatus: Live

Job Summary

Build and scale manufacturing data ecosystems by designing pipelines and data architectures that ingest, consolidate, and enable real-time analytics. Lead advanced ML and statistical modeling for problems like yield optimization and predictive maintenance. Create agentic, autonomous automation workflows for feature engineering, model execution, and decision-making at scale, partnering with manufacturing, supply chain, and engineering teams to deliver measurable operational outcomes. Communicate insights to senior leadership and help mentor multi-disciplinary teams.
Location: Singapore
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Sr. Manager level

Key Responsibilities

  • •Architect and scale manufacturing data ecosystems to ingest, consolidate, and analyze structured and unstructured data from plant, supply chain, and engineering sources.
  • •Lead analytics and modeling using statistical methods, machine learning, and data mining to address yield optimization, predictive maintenance, and constraint resolution.
  • •Build agentic workflows and autonomous, AI-powered pipelines that orchestrate ingestion, feature engineering, model execution, and decision-making at scale.
  • •Define and optimize data architecture and scalable data models supporting real-time analytics, digital twins, and AI-driven manufacturing use cases.
  • •Translate analytics into business outcomes by partnering with manufacturing operations, supply chain, and product engineering teams to define KPIs, uncover insights, and operationalize recommendations.
Travel: Low travel

Pay and Benefits

Perks:Wellness StipendLearning BudgetTravel Allowance

Key Requirements

  • •10+ years of experience with agent-based systems, AI orchestration frameworks, and workflow automation to deliver scalable and reusable analytics solutions.
  • •Deep expertise in advanced analytics, AI/ML, and industrial data systems across domains such as supply chain, quality, and engineering.
  • •Ability to define and optimize data architecture, including data acquisition strategies, semantic layers, and scalable data models for real-time analytics and digital twins.
  • •Experience applying statistical, machine learning, and data mining techniques to solve manufacturing problems (e.g., yield optimization, predictive maintenance, constraint resolution).
  • •Strong executive communication and stakeholder alignment; ability to lead and mentor multi-disciplinary teams to deliver production-ready solutions.
Experience:ManufacturingSemiconductorsIndustrial dataSupply chainDigital twins
Skills:CommunicationStakeholder managementProblem-solvingMentoringCross-functional collaboration
Tech Stack:PythonRSQLMachine learningAI/MLData miningSemantic layers

Company Brief

Applied Materials
Provides semiconductor manufacturing equipment, services, and software used to build advanced chips and display technologies. Its tools support materials engineering, deposition, etching, inspection, and process control for major electronics manufacturers worldwide.
Industry: Industrial Machinery
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Santa Clara, United States
Founded: 1967
Glassdoor
Glassdoor: 3.9
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