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Machine Learning Surrogate Models Jobs (NOW HIRING)

Amat

Physicist/Scientist Machine Learning

Santa Clara, CA · On-site +1

$138K - $190K/yr

Exposure to physicsinformed machine learning (PIML) , surrogate modeling, reducedorder modeling, or operator learning * Publications or demonstrated research contributions in ML for physical systems ...

EarnIn

Machine Learning Intern

Mountain View, CA · Hybrid

$40/hr

POSITION SUMMARY As a Fintech company where Machine Learning (ML) is one of the key drivers of growth, our operations highly rely on machine learning models, from business decisions to customer ...

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

Nanite Inc.

Machine Learning Engineer

Boston, MA

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

Alignerr

Machine Learning Engineer

Seattle, WA · Remote

Machine Learning Engineer (AI Data Trainer) About the Role What if your expertise in machine ... You won't be building models from scratch - you'll be teaching them how to think by producing the ...

Dark Wolf

Machine Learning Engineer

Herndon, VA · On-site

Responsibilities : • Design, develop, and implement machine learning models and algorithms to solve specific business problems. • Build and maintain scalable and robust machine learning pipelines ...

Beyond SOF

Engineer, Machine Learning

Washington, DC · On-site

The Machine Learning Engineer is responsible for developing and implementing machine learning models and algorithms to solve complex problems. Main Responsibilities and Duties: * Develop and ...

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Machine Learning Surrogate Models information

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$25.5K

$42.6K

$88K

How much do machine learning surrogate models jobs pay per year?

As of Jun 4, 2026, the average yearly pay for machine learning surrogate models in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Surrogate Models Specialist, and why are they important?

To thrive in the field of Machine Learning Surrogate Models, you need a strong background in mathematics, statistics, and computer science, typically with an advanced degree in a related field. Expertise in machine learning frameworks (such as TensorFlow or PyTorch), experience with numerical simulation tools, and familiarity with surrogate modeling techniques are essential. Analytical thinking, problem-solving abilities, and effective communication help interpret complex data and collaborate with multidisciplinary teams. These skills are crucial for efficiently developing accurate surrogate models that accelerate simulations and optimize solutions in research and industry.

What are some common challenges faced when developing machine learning surrogate models, and how are they typically addressed?

Developing machine learning surrogate models often involves challenges such as handling limited or noisy training data, ensuring model generalization, and balancing prediction accuracy with computational efficiency. Practitioners typically address these issues by carefully selecting appropriate algorithms (such as Gaussian processes or neural networks), employing cross-validation techniques, and using domain knowledge to inform feature engineering. Collaboration with domain experts is also crucial to ensure the surrogate model accurately represents the underlying system and meets project requirements.

What are machine learning surrogate models?

Machine learning surrogate models are simplified models that approximate the behavior of more complex and computationally expensive simulations or processes. They are used to provide fast predictions or analyses by learning patterns from data generated by the original, high-fidelity models. Surrogate models are often employed in engineering, optimization, and scientific research to reduce computation time while maintaining reasonable accuracy. Common machine learning techniques used for surrogate modeling include Gaussian Processes, neural networks, and support vector machines.

What is the difference between Machine Learning Surrogate Models vs Data Scientists?

AspectMachine Learning Surrogate ModelsData Scientists
CredentialsTypically require knowledge of machine learning, programming, and domain expertiseRequire degrees in data science, statistics, or related fields, often with certifications
Work EnvironmentFocus on developing models to approximate complex systems, often in engineering or simulation contextsAnalyze data, develop insights, and create predictive models across various industries
Industry UsageUsed in engineering, manufacturing, and simulation-heavy sectorsWidely used across finance, healthcare, marketing, and technology

Machine Learning Surrogate Models are specialized tools for approximating complex systems, often in engineering contexts, while Data Scientists analyze and interpret data to inform business decisions across diverse industries. Both roles require strong analytical skills but differ in focus and application.

Infographic showing various Machine Learning Surrogate Models job openings in the United States as of May 2026, with employment types broken down into 25% Internship, and 75% Full Time. Highlights an 75% In-person, and 25% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Physicist/Scientist Machine Learning

Amat

Santa Clara, CA • On-site, Remote

$138K - $190K/yr

Full-time

Posted 16 days ago

Job description

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

Salary:

$138,000.00 - $190,000.00

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.

We are seeking a highly motivated MS or PhDlevel scientist or engineer to develop and apply machine learning-based models using data generated from multidimensional, highperformance computing (HPC) simulations. The successful candidate will work at the intersection of physicsbased modeling, largescale simulation, and modern AI/ML methods to accelerate product developing in the fast-paced semiconductor equipment industry. Focus will be on developing ML models based on plasma and electromagnetic simulations.

This role is ideal for candidates with strong domain knowledge in engineering or physical sciences and handson experience translating complex simulation data into robust, predictive machine learning models.

Required Qualifications

  • MS or PhD in Engineering (e.g., Chemical, Electrical, Mechanical, Aerospace, Nuclear, Materials), Science (e.g., Physics, Chemistry), or Computer Science
  • Significant experience developing machine learning or deep learning models using data from multidimensional numerical simulations (e.g., PDEbased solvers, particlebased simulations, multiphysics models)
  • Strong background in Pythonbased scientific computing and ML workflows
  • Demonstrated experience with PyTorch or equivalent deep learning frameworks
  • Solid understanding of:
    • Data preprocessing and feature engineering for large, highdimensional datasets
    • Model training, validation, and performance evaluation
    • Numerical methods and/or physicsbased modeling concepts

Preferred Qualifications

  • Experience with NVIDIA Physics NeMo, NVIDIA Modulus, or related physicsinformed or simulationdriven ML libraries
  • Familiarity with GPUaccelerated computing, CUDAaware workflows, and HPC environments
  • Exposure to physicsinformed machine learning (PIML), surrogate modeling, reducedorder modeling, or operator learning
  • Publications or demonstrated research contributions in ML for physical systems or related fields

Key Responsibilities

  • Develop and train machine learning and deep learning models using data from largescale, multidimensional HPC simulations
  • Collaborate with domain experts to incorporate physical constraints, scientific insight, and prior knowledge into ML model design
  • Design workflows for data ingestion, curation, and analysis of highvolume simulation outputs
  • Evaluate model accuracy, generalization, and robustness across a wide range of operating conditions
  • Optimize models for performance, scalability, and deployment on GPUaccelerated platforms
  • Contribute to internal software tools, modeling frameworks, and best practices

Additional Information

Time Type:

Full time

Employee Type:

New College Grad

Travel:

Yes, 10% of the Time

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.

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