14 jobs for "Machine Learning Engineer II"

Who We Are

Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world's technology.

What We Offer

Salary:
$131,000.00 - $180,000.00

Location:
Santa Clara,CA

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.

What You'll Do

As an Algorithm Developer Engineer E2, you will contribute to the research and development of computer vision, image analysis, and signal processing algorithms for semiconductor wafer inspection and metrology applications. Working closely with experienced engineers and researchers, you will implement and optimize algorithms that enhance defect detection, classification, and metrology accuracy across Applied Materials' semiconductor inspection and metrology products.

Key Responsibilities

• Research, develop, prototype, and evaluate computer vision, image analysis, signal processing, and machine learning algorithms for semiconductor wafer inspection and metrology applications.

• Conduct benchmarking and validation studies to evaluate algorithm accuracy, robustness, computational efficiency, and overall performance.

• Implement, optimize, and maintain product-quality algorithmic software in Python and C++ for production and deployment.

• Collaborate with software, systems, applications, and compute infrastructure teams to integrate algorithms into production products.

• Document technical findings and communicate results through reports, presentations, and design reviews.

Minimum Qualifications

• Recent Master's or Ph.D. graduate in Electrical Engineering, Computer Science, or a related field, with specialization in Computer Vision, Image Processing, Signal Processing, Machine Learning, or related areas.

• Graduate-level coursework in classical and modern computer vision, along with hands-on project work in image registration, image segmentation, camera calibration, multi-view geometry, and related image analysis techniques.

• Proficient in Python and/or C++ through research, coursework, internships, or academic projects.

Preferred Qualifications

• Experience applying mathematical and statistical methods to formulate and solve engineering problems.

• Experience with image-based deep learning methods for computer vision applications.

• Experience using AI-assisted coding tools for software development, prototyping, and debugging.

• Publications in leading peer-reviewed conferences or journals (e.g., CVPR, ICCV, ECCV, ICIP, or similar venues).

Additional Skills

• Familiarity with machine learning frameworks such as PyTorch, TensorFlow, or equivalent tools.

• Strong analytical, problem-solving, and communication skills.

• Ability to work effectively in a collaborative, cross-functional engineering environment.

Qualifications

Education: Master's Degree or Ph.D.

Years of Experience: 0-2 Years

Work Experience: New College Graduate

Additional Information

Travel: Up to 10%

Relocation Eligible: Yes

Hiring Profile Summary

This position targets high-potential Master's and Ph.D. graduates with strong foundations in computer vision, image processing, signal processing, and machine learning. The role focuses on the research, development, validation, optimization, and deployment of algorithms for semiconductor wafer inspection and metrology applications while providing opportunities to work closely with multidisciplinary engineering teams to solve challenging real-world manufacturing problems.

Additional Information

Time Type:
Full time

Employee Type:
New College Grad

Travel:
Not Specified

Relocation Eligible:
Yes

The salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable.

For all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement.

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.

In addition, Applied endeavors to make our careers site accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at Accommodations_Program@amat.com, or by calling our HR Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.

About Applied Materials

At Applied Materials, our innovations Make Possible® a Better Future.

Size: 10,000+ employees
Industry: Manufacturing

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