Customer Engineer - Data Science / ML DevOps Internship

Posted 3 Days Ago
Be an Early Applicant
Singapore, SGP
In-Office
Internship
Artificial Intelligence • Semiconductor • Manufacturing
The Role
Intern will evaluate and improve model governance and management practices for predictive maintenance models. They will collaborate with data scientists and equipment experts to pilot an end-to-end ML management framework, document best practices, analyze data, interpret experimental results, and present actionable recommendations supporting operational efficiency and lean manufacturing.
Summary Generated by Built In

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


Location:

Singapore,SGP

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

Project Description:

Predictive models provide value for field service engineers to be able to move from break-fix ways of maintenance to a more proactive maintenance approach. Accordingly, as the number of models increase, model governance and management become crucial to sustain the value of the models to the end users. The intern will study the existing approach and propose improvements to the current situation. The intern will collaborate with data scientists and equipment experts to pilot a framework for end to end model management along with documentation of best practices.

Preferred Discipline:

Data Science & Analytics

Computer Engineering

Desired Skills Required:

Programming in Python; Basic knowledge of machine learning and data analysis; Understanding of physics principles related to sensing and measurement; Familiarity with data visualization tools; Ability to interpret experimental results and draw conclusions. Familiarity with machine learning workflows and ML DevOps best practices.
 

Learning Outcome:

The intern will gain practical experience in process improvement methodologies and digital transformation within an operational setting. They will develop skills in stakeholder engagement, data collection, and analysis, and will learn how to translate process insights into actionable recommendations.  They will gain firsthand experience applying industrial engineering methodologies in a live production environment, learn to present data-backed recommendations to stakeholders, and understand how systematic analysis contributes to operational efficiency and lean manufacturing goals

Additional Information

Time Type:

Full time

Employee Type:

Intern / Student

Travel:

No

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.

Skills Required

  • Background in Data Science and Analytics
  • Background in Computer Engineering
  • Programming proficiency in Python
  • Basic knowledge of machine learning and data analysis
  • Understanding of physics principles related to sensing and measurement
  • Familiarity with data visualization tools
  • Ability to interpret experimental results and draw conclusions
  • Familiarity with machine learning workflows and ML DevOps best practices

Applied Materials Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Applied Materials and has not been reviewed or approved by Applied Materials.

  • Healthcare Strength Company materials emphasize comprehensive medical, dental, vision, mental health, and wellness programs for employees and families, with coverage beginning on day one in many cases. On-site or virtual care options at major campuses further reinforce the breadth of support.
  • Leave & Time Off Breadth Exempt employees are offered a Flexible Time Off program and U.S. teams observe 11 paid company holidays. These policies are consistently highlighted across official benefits summaries.
  • Equity Value & Accessibility An Employee Stock Purchase Plan is widely available and presented as a core part of the U.S. package, alongside equity grants in many roles. These ownership elements are positioned as meaningful contributors to total rewards.

Applied Materials Insights

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The Company
HQ: Santa Clara, CA
23,282 Employees
Year Founded: 1969

What We Do

Applied Materials is the leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. Our expertise in modifying materials at atomic levels and on an industrial scale enables customers to transform possibilities into reality. At Applied Materials, our innovations make possible a better future.

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