Who We Are

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips – the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world – like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world.

What We Offer

Location:

Rehovot,ISR

You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more.

At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits.

Role Description

We are seeking a hands‑on Deep Learning Expert to create and productize innovative AI solutions. You will lead models from idea to production—designing breakthrough approaches, validating them rigorously, and packaging them into reliable, scalable components. The role blends research‑driven experimentation with pragmatic engineering: you’ll define data strategies, craft novel architectures, and build monitoring/explainability so new capabilities can be trusted in real‑world use. You’ll collaborate with software, product, and testing partners to translate domain challenges into AI features that elevate performance, robustness, and ease of use.

Key Responsibilities

• Invent and advance novel AI methods: propose, prototype, and validate new architectures and training strategies that outperform current solutions.
• Build high‑quality models and pipelines: design datasets, augmentations, and reproducible training/evaluation workflows optimized for experimentation and rapid iteration.
• Productize for scale: package models with clean APIs and documentation; integrate into production systems with reliability, performance, and maintainability in mind.
• Trust & governance: implement monitoring (confidence scoring, out‑of‑distribution detection, drift alerts) and benchmarking to prove impact and sustain quality over time.
• Explainability & adoption: develop intuitive tools and visualizations to clarify model behavior and accelerate stakeholder and customer buy‑in.
• Cross‑functional collaboration: partner with software, product, and testing teams to align requirements, gather feedback, and deliver high‑impact releases.
  
  

Requirements

• M.Sc. or Ph.D in Physics, Mathematics , Computer Science or related field
• 5–8+ years building and shipping deep learning solutions (vision focus preferred).
• Strong proficiency in Python and DL frameworks as PyTorch.
• Proven experience taking models from prototype to production (packaging, deployment, runtime integration).
• Practical knowledge of model monitoring (confidence/OOD/drift), benchmarking, and performance tuning.
• Solid foundations in computer vision (e.g., segmentation, registration).
• Comfort with Linux/GPU development environments and modern IDE/tooling.
• Clear communication skills; ability to collaborate across software, product, and testing.
• Track record of publishing, patenting, or open‑sourcing innovative AI work.
  
  

Additional Information

Time Type:

Full time

Employee Type:

Assignee / Regular

Travel:

Yes, 10% of the Time

Relocation Eligible:

No

Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.