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Model Predictive Control Jobs in Phoenix, AZ (NOW HIRING)

Advantest

Thermal Controls R&D Engineer

Chandler, AZ · On-site

$81K - $105K/yr

This role involves designing, modeling, and implementing control algorithms for complex thermal ... Support development of digital twins and predictive maintenance strategies using machine learning.

Advantest

Thermal Controls R&D Engineer

Chandler, AZ · On-site

$81K - $105K/yr

This role involves designing, modeling, and implementing control algorithms for complex thermal ... Support development of digital twins and predictive maintenance strategies using machine learning.

Carvana

Senior Data Engineer, Predictive Modeling

Tempe, AZ · On-site

$101K - $137K/yr

Work Model: This is a 100% on-site role at our Tempe office, Monday through Friday. About the team ... Apply software engineering best practices including code reviews, version control, testing ...

Carvana

Senior Data Engineer, Predictive Modeling

Tempe, AZ · On-site

$101K - $137K/yr

Work Model: This is a 100% on-site role at our Tempe office, Monday through Friday. About the team ... Apply software engineering best practices including code reviews, version control, testing ...

USAA

Data Scientist Senior

Phoenix, AZ · On-site +1

... predictive modeling solutions that drive performance, support risk management, and create ... Develops and deploys models within the Model Development Control (MDC) and Model Risk Management ...

USAA

Data Scientist Senior

Phoenix, AZ · On-site +1

... predictive modeling solutions that drive performance, support risk management, and create ... Develops and deploys models within the Model Development Control (MDC) and Model Risk Management ...

TSMC

Quality Management Engineer

Phoenix, AZ · On-site

$88K - $114K/yr

Standardizing, and documenting control models and methodologies. * Predictive analytics and statistical applications including modeling, machine learning, data science, process control methods, and ...

TSMC

Quality Management Engineer

Phoenix, AZ · On-site

$88K - $114K/yr

Standardizing, and documenting control models and methodologies. * Predictive analytics and statistical applications including modeling, machine learning, data science, process control methods, and ...

Fox

Senior Broadcast Reliability Engineer

Tempe, AZ · On-site

Experience leveraging generative AI and large language models (LLMs) to accelerate software ... predictive monitoring, intelligent alerting, root cause analysis, and automated remediation

New

Stellar IT Group

ServiceNow Architect

Chandler, AZ · On-site

$70.25 - $88.50/hr

... models, integrations, and workflows. * Governance & Security: Establish governance processes to ... In-depth knowledge of ServiceNow update sets, version control, deployment processes, and platform ...

ProKatchers LLC

Stormwater Engineer, Sr.

Scottsdale, AZ · On-site

$115.07/hr

... predictive algorithms based on proprietary behavioral, psychological and personality testing ... The position also participates with management of drainage and flood control studies, manages the ...

ProKatchers LLC

Stormwater Engineer, Sr.

Scottsdale, AZ

$115.07/hr

... predictive algorithms based on proprietary behavioral, psychological and personality testing ... The position also participates with management of drainage and flood control studies, manages the ...

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Showing results 1-20

All JobsModel Predictive Control JobsModel Predictive Control Jobs in Phoenix, AZ

Model Predictive Control information

See Phoenix, AZ salary details

$54.6K

$95.9K

$130.1K

How much do model predictive control jobs pay per year?

As of Jul 13, 2026, the average yearly pay for model predictive control in Phoenix, AZ is $95,889.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,900.00 and $107,200.00 per year, depending on experience, location, and employer.

What is Model Predictive Control?

Model Predictive Control (MPC) is an advanced method of process control that uses a mathematical model to predict and optimize the future behavior of a system. It works by solving an optimization problem at each control step to determine the best sequence of control actions, taking into account system constraints and objectives. MPC is widely used in industries such as chemical processing, energy, and automotive because it can handle multivariable control problems and anticipate future events. Its predictive nature allows for improved performance, stability, and efficiency compared to traditional control methods.

What is the difference between Model Predictive Control vs Control Systems Engineer?

AspectModel Predictive ControlControl Systems Engineer
CredentialsEngineering degree, control theory, process modelingEngineering degree, control systems, automation
Work EnvironmentIndustrial automation, process control, manufacturingDesign, develop, and maintain control systems across industries
Industry UsageProcess industries, chemical, oil & gas, manufacturingAutomation, robotics, embedded systems, industrial sectors

Model Predictive Control (MPC) focuses on advanced control algorithms for optimizing processes, while Control Systems Engineers design and implement various control systems. MPC is a specialized skill within control engineering, often requiring knowledge of process modeling and optimization, whereas Control Systems Engineers have broader responsibilities across multiple control technologies. Both roles are essential in industrial automation but differ in scope and application.

What are the typical challenges faced by engineers working with Model Predictive Control (MPC) systems in an industrial setting?

Engineers working with Model Predictive Control systems often encounter challenges related to model accuracy, computational demands, and real-time implementation. Ensuring the process model accurately represents the plant dynamics is critical, as discrepancies can lead to suboptimal control performance. Additionally, MPC algorithms can be computationally intensive, particularly for large-scale or fast processes, requiring careful tuning and optimization to maintain real-time operation. Collaboration with process engineers and IT specialists is common, as integrating MPC with existing control systems and plant infrastructure is a key part of the role.

What are the key skills and qualifications needed to thrive as a Model Predictive Control (MPC) Engineer, and why are they important?

To thrive as a Model Predictive Control Engineer, you need strong foundations in control theory, applied mathematics, and process engineering, usually supported by a degree in engineering or a related field. Proficiency with simulation tools such as MATLAB/Simulink, programming languages like Python or C++, and familiarity with industrial automation systems are typically required. Analytical thinking, problem-solving abilities, and effective communication skills help distinguish top performers in this role. These skills are essential for designing, implementing, and optimizing advanced control algorithms that improve system performance and reliability in complex industrial environments.
What are popular job titles related to Model Predictive Control jobs in Phoenix, AZ? For Model Predictive Control jobs in Phoenix, AZ, the most frequently searched job titles are:
What job categories do people searching Model Predictive Control jobs in Phoenix, AZ look for? The top searched job categories for Model Predictive Control jobs in Phoenix, AZ are:
What cities near Phoenix, AZ are hiring for Model Predictive Control jobs? Cities near Phoenix, AZ with the most Model Predictive Control job openings:
Model Predictive Control jobs near you
Thermal Controls R&D Engineer

Thermal Controls R&D Engineer

Advantest

Chandler, AZ • On-site

$81K - $105K/yr

Full-time

Re-posted 11 days ago

Job description

Advantest America is a leading provider of semiconductor test and measurement solutions. As part of our commitment to innovation, we are expanding our Global Thermal R&D Team to develop advanced thermal control strategies for next-generation semiconductor test environments.

We are seeking a highly skilled Thermal Controls R&D Engineer with expertise in control systems engineering, including both classical control theory and modern AI/ML-based approaches. This role involves designing, modeling, and implementing control algorithms for complex thermal systems, ensuring precise temperature regulation under dynamic conditions. The ideal candidate will have strong practical experience combined with simulation and analytical skills to drive innovation in thermal management.

Essential Duties & Responsibilities

  • Design and implement control algorithms for thermal systems, including heaters, chillers, and two-phase cooling loops.
  • Develop simulation models for thermal dynamics and control performance using tools such as MATLAB/Simulink or equivalent.
  • Apply classical control theory (PID, state-space, adaptive control) and advanced techniques (AI/ML-based predictive control) to optimize system response.
  • Integrate control systems into hardware platforms and validate performance through experimental testing.
  • Collaborate with cross-functional teams to ensure seamless integration of thermal controls into semiconductor test equipment.
  • Analyze system data to improve control strategies and enhance reliability, efficiency, and robustness.
  • Support development of digital twins and predictive maintenance strategies using machine learning.

Why Join Us?

This is a rare opportunity to be part of a global initiative shaping the future of thermal technologies in semiconductor testing. You'll work with cutting-edge tools and collaborate with some of the brightest minds in the industry, driving innovation that directly impacts next-generation test solutions.

Requirements, Education & Skills

  • Bachelor's degree in Mechanical Engineering, Electrical Engineering, Control Systems, or related field; Master's degree preferred.
  • 5+ years of experience in control systems engineering, preferably in thermal or process control applications.
  • Strong knowledge of classical control theory and practical implementation of PID and advanced controllers.
  • Experience with AI/ML techniques for predictive control and optimization.
  • Proficiency in simulation tools (MATLAB/Simulink, Modelica, or similar).
  • Familiarity with thermal systems, including heaters, chillers, and phase-change cooling technologies.
  • Hands-on experience with sensors, actuators, and embedded control hardware.
  • Excellent analytical, problem-solving, and communication skills.
  • Ability to work effectively in global, cross-functional teams.
  • Willingness to travel domestically and internationally (up to 10%).
  • On-site role based at our Lake Forest, CA facility.

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