ZipRecruiter

Log In

1

Machine Learning Surrogate Models Jobs (NOW HIRING)

Dark Wolf

Machine Learning Engineer

Chantilly, 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

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 implement ...

The Pennsylvania State University

... and machine learning (ML) applied to numerical weather prediction and data assimilation. In ... ML surrogate models for the atmosphere and sea surface as part of the ensemble component of the ...

SAIC

Machine Learning Developer

Arlington, VA · Hybrid

Develop and implement machine learning models and algorithms to provide suggested values to readiness reports for our DOD client. * Refine data collection processes and improve data quality. * Design ...

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 implement ...

SAIC

Machine Learning Developer

Arlington, VA · Hybrid

Develop and implement machine learning models and algorithms to provide suggested values to readiness reports for our DOD client. * Refine data collection processes and improve data quality. * Design ...

Full Scope

Machine Learning Engineer

Reston, VA · On-site

Design, develop, and implement machine learning models and algorithms to solve real-world problems. * Collaborate with cross-functional teams to understand business requirements and translate them ...

next page

Showing results 1-20

People also search for

All JobsMachine Learning Surrogate Models Jobs

Machine Learning Surrogate Models information

See salary details

$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.

Machine Learning Model Engineer

Samsung Electronics America North America

Mountain View, CA • On-site

$240K - $280K/yr

Other

This job post has expired today. Applications are no longer accepted.

Job description

Samsung Ads is an advanced advertising technology company in rapid growth that focuses on enabling brands to connect with Samsung TV audiences as they are exposed to digital media by using the industry’s most comprehensive data to build the world’s smartest advertising platform. Being part of an international company such as Samsung and doing business worldwide means that we get to work on the most challenging projects with stakeholders and teams around the globe.

We are proud to have built a world-class organization grounded in an entrepreneurial and collaborative spirit. Working at Samsung Ads offers one of the best environments in the industry to learn just how fast you can grow, how much you can achieve, and how good you can be. We thrive on problem-solving, breaking new ground, and enjoying every part of the journey.

Machine learning lies at the core of the advertising industry. This is no exception to Samsung Ads. At Samsung Ads, we actively explore the latest machine learning techniques to improve our existing systems and products and create new revenue streams. As a machine learning model engineer of the Samsung Ads Platform Intelligence (PI) team, you will have access to unique Samsung proprietary data to develop and deploy a wide spectrum of large-scale machine learning products with real-world impact. You will work closely with and be supported by a talented engineering team and top-notch researchers to work on exciting machine learning projects and state-of-the-art technologies. A unique learning culture and creative work atmosphere will welcome you. This is an exciting and unique opportunity to get deeply involved in envisioning, designing, and implementing cutting-edge machine learning products with a growing team.

Responsibilities

  • Lead a team to deliver production-grade machine learning solutions with notable business impact from end to end.

  • Design, develop, and deploy scalable low-latency machine learning products

  • Communicate with various stakeholders to understand business requirements, manage expectations, and create effective roadmaps

  • Closely work with machine learning platform and serving teams to deploy and streamline machine learning pipelines

  • Optimize and scale up existing machine learning products

  • Closely work with the MLOps team to ensure product health

  • Closely work with external partners to introduce new machine learning features and tools

  • Research the latest machine learning technologies and keep up-to-date with industry trends and developments

  • Create quick prototypes and proof-of-concepts for new features

  • Design and implement next-generation machine learning models with advanced technologies

Experience Requirements:

  • Master’s or PhD degree in Computer Science or related fields

  • 5+ years of industry experience with a Master’s degree or 3+ years of industry experience with a PhD degree

  • Solid theoretical background in machine learning and/or data mining

  • Rich hands-on experience with production-grade machine learning solutions

  • Proficiency in mainstream ML libraries (e.g., TensorFlow, PyTorch, Spark ML, etc.)

  • Experience with mainstream big data tools (e.g., MapReduce, Spark, Flink, Kafka, etc.)

  • Extensive programming experience in Python, Go, or other OOP languages

  • Familiarity with data structures, algorithms, and software engineering principles

  • Proficiency in SQL and databases

  • Strong communication and interpersonal skills to drive cross-functional partnerships

Preferred Experience Requirements:

  • Publications in top relevant venues (e.g., TPAMI, NeurIPS, ICML, ICLR, KDD, WWW, AAAI, IJCAI, etc.)

  • Basic knowledge about Amazon Web Services (AWS)

  • Experience with the advertising industry and real-time bidding (RTB) ecosystem

CALIFORNIA ONLY

Compensation for this role is expected to be between $240,000 and $280,000. Actual pay will be determined considering factors such as relevant skills and experience, and comparison to other employees in the role.

#LI-JT1

Apply