AI-Driven Manufacturing Scheduling Engineer

Applied Materials; Austin (TX); On-site; USD 124,000 - 171,000; Full time

Benefits offered by this job

Stock award program

Bonus potential

Comprehensive benefits

Career development

Job summary

Applied Materials is a global leader in materials science and engineering solutions for semiconductors and displays, based in Austin. This role focuses on scheduling, planning, and optimization across manufacturing and supply chains, leveraging AI-enabled decision-support and modern data platforms.

The team partners with scheduling planners, engineering, data scientists, and operations leaders to deliver scalable solutions that improve cycle time, throughput, and delivery performance while

Qualifications

• Deep expertise in manufacturing scheduling, planning, and operations research.
• Experience with optimization techniques and AI-enabled decision support systems.

Responsibilities

• Collaborate with Scheduling, Manufacturing, and business stakeholders to translate objectives into scalable scheduling solutions.
• Serve as liaison between scheduling teams and engineering to improve cycle time, tool utilization, and throughput.
• Develop and deploy analytical models, automation frameworks, and AI-enabled scheduling solutions using Python, SQL, and cloud platforms.
• Design dashboards and decision-support tools with product owners, software engineers, and data scientists.
• Drive proof-of-concepts, pilots, and production rollouts delivering measurable business value.

Skills

Scheduling

Operations research

AI-enabled tools

Data analysis

Cross-functional collaboration

Tools

Python

SQL

Optimization

Simulation tools

Cloud platforms

Job description

Applied Materials is a global leader in materials science and engineering solutions for semiconductors and displays, based in Austin. This role focuses on scheduling, planning, and optimization across manufacturing and supply chains, leveraging AI-enabled decision-support and modern data platforms.

The team partners with scheduling planners, engineering, data scientists, and operations leaders to deliver scalable solutions that improve cycle time, throughput, and delivery performance while