AI-Driven Scheduling Engineer — Forward Deployed

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

Job summary

Applied Materials, Inc. is seeking a seasoned scheduling and planning professional in Austin to design and deploy AI-driven scheduling solutions. You will partner with scheduling, manufacturing, and business teams to translate complex objectives into scalable software and processes.

You will develop dashboards, models, and automation to improve cycle time, tool utilization, throughput, and delivery performance across the value chain.

Qualifications

• Demonstrated expertise in scheduling, planning processes, and optimization techniques.
• Strong understanding of capacity planning and factory operations.
• Experience with AI-enabled decision support systems is a plus.

Responsibilities

• Partner with Scheduling, Manufacturing, and Business stakeholders to translate objectives into scalable solutions.
• Serve as main liaison between scheduling teams and engineering to improve cycle time and throughput.
• Develop and deploy AI-enabled scheduling tools, dashboards, and workflows.
• Collaborate with product, data science, and operations leaders to implement decision-support systems.
• Analyze data from manufacturing, supply chain, and operations to identify bottlenecks and opportunities.
• Drive proof-of-concept, pilot, and production deployments delivering measurable value.
• Communicate findings and recommendations to leadership to influence roadmap decisions.

Skills

Scheduling

Operations planning

Optimization

AI scheduling

Data analysis

Tools

Python

SQL

Optimization algorithms

Simulation tools

Cloud platforms

Job description

Applied Materials, Inc. is seeking a seasoned scheduling and planning professional in Austin to design and deploy AI-driven scheduling solutions. You will partner with scheduling, manufacturing, and business teams to translate complex objectives into scalable software and processes.

You will develop dashboards, models, and automation to improve cycle time, tool utilization, throughput, and delivery performance across the value chain.