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

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 ...

Intel

Data Scientist/Statistician

Phoenix, AZ · On-site

$116K - $228K/yr

Drives organization to use process control systems to improve capability, matching, and stability of semiconductor process technologies * Use predictive modeling, statistics, Machine Learning, Data ...

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

Model Predictive Control information

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$54.6K

$95.9K

$130.1K

How much do model predictive control jobs pay per year?

As of Sep 14, 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 are the typical challenges faced by engineers working with model predictive control 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 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 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 does a model predictive control do?

A Model Predictive Control (MPC) engineer designs control systems that use a mathematical model to predict future system behavior and optimize control actions accordingly. MPC is commonly used in industries like process control and robotics, requiring skills in control theory, programming, and system modeling. The role involves developing algorithms, tuning controllers, and ensuring system stability and efficiency.

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
Infographic showing various Model Predictive Control job openings in Phoenix, AZ as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 16% Part Time, 2% Contract, and 1% Nights. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $95,889 per year, or $46.1 per hour.

Advanced AI Engineer - Mechanical Engineering

Tempe, AZ • On-site

Honeywell International, Inc.
Furniture Manufacturing • 10K+ employees
Learn more about Honeywell International, Inc.external

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 9 days ago

Honeywell rating

8.3

Company rating: 8.3 out of 10

Based on 186 frontline employees who took The Breakroom Quiz

Job description


As an Advanced AI Engineer at Honeywell Aerospace, you will provide technical contributions to the development of AI-driven engineering design tools and physics-based machine learning models. This role focuses on applying artificial intelligence, physics-informed methods, and surrogate modeling to accelerate the design and analysis of aerospace mechanical systems, including wheels and brakes, fuel systems, environmental control systems, and other complex components. You will help shape the next generation of simulation and analysis capabilities by integrating AI with traditional computational tools such as CFD, FEA, and multi-physics solvers.
In this role, you will develop advanced AI surrogates and Physics-AI models, and contribute to the transition these technologies into engineering workflows across Honeywell Aerospace. You will collaborate with cross-functional teams and global research partners, pursue both internal and government research funding, and contribute to the execution from concept through integration. Your work will impact engineering efficiency, product performance, and Honeywell's leadership in AI-enabled engineering design.
Key Responsibilities
  • Contribute to the creation of advanced Physics-AI models and surrogate models to accelerate engineering workflows for CFD, thermal analysis, structural analysis, and system-level simulation.
  • Create scripted FEA or CFD models to generate training data over parameter and load condition spaces.
  • Suggest ideas to drive the AI strategy for engineering design, with a focus on physics-informed neural networks (PINNs), digital twins, and high-fidelity model surrogates.
  • Research and test new AI methodologies for multi-physics modeling of aerospace components such as engines, wheels and brakes, and mechanical actuation systems.
  • Suggest improvements to current processes for efficient data generation, data handling, and model utilization.
  • Utilize and advance state-of-the-art NVIDIA simulation and AI acceleration tools, including Physics NEMO and related model-based AI frameworks.
  • Collaborate closely with engineering teams to integrate surrogate models into design processes, enabling faster trade studies, optimization, and predictive analysis.
  • Contribute to technical execution across internal and government-sponsored R&D projects and contribute to proposal development.
  • Assist with outreach to traditional design and analysis engineering functions.

Qualifications
YOU MUST HAVE
  • Bachelor's degree from an accredited institution in technical disciplines such as the sciences, technology, engineering or mathematics.
  • 2 years of experience developing AI models for physics-based simulation, engineering analysis, multi-physics modeling, or surrogate modeling. Experience in a graduate program may be included.
  • 3 years with simulation scripting (Abaqus Python scripting interface, Ansys PyAnsys or APDL or similar open-source tools).
  • 5 years working on design and simulation of physics of engineering systems involving concepts such as Computational Fluid Dynamics or Structural Analysis
  • Experience mentoring others in specialty areas.
  • Experience with NVIDIA's physics-accelerated AI tools such as Physics NEMO, Modulus, Warp, or similar platforms for physics-informed deep learning

WE VALUE
  • Bachelor's or Master's degree in aerospace engineering, mechanical engineering, or a related engineering discipline.
  • Proficiency in Python and machine learning frameworks such as PyTorch and TensorFlow.
  • Experience with JAX for differentiable models
  • Experience working in structured machine learning deployment environments i.e. MLOps workflows
  • Experience with CFD, structural analysis, thermal modeling, or multi-physics simulation, and the ability to couple these with AI-based surrogates.
  • Experience with AI model architectures used for surrogate modeling, such as but not limited to MeshGraphNets, Neural Operators, Physics-Informed and Physics-Attention models.
  • Experience with model visualization through tools such as PyVista, Matplotlib, Plotly, and others.
  • Awareness of considerations for deploying AI models into engineering design workflows or digital engineering ecosystems.
  • Awareness of current research in physics-informed ML, scientific machine learning, and surrogate modeling at major conferences and journals.

ABOUT HONEYWELL AEROSPACE
Join a company that's reintroducing itself to the aviation community we've helped advance for more than a century. At Honeywell Aerospace (NASDAQ: HONA), we're launching as an independent, publicly traded aerospace and defense company built on a legacy of operational excellence and mission-focused execution.
Our new brand identity pairs that heritage with real momentum, as we build technology that helps pilots navigate with confidence, aircraft operate more efficiently, and operators stay ahead of change. With our systems on board 90% of the world's aircraft, your work here has reach, that's rare to find anywhere else.
Focusing on our customers, investing in innovation, and building a culture of accountability and performance is how we're shaping what comes next.
Every horizon. Every mission. Every day.
BENEFITS OF WORKING FOR HONEYWELL AEROSPACE
Beyond a performance-driven salary, you'll work alongside dedicated experts on technology that's advancing aviation. As a Honeywell Aerospace employee, you're eligible for a comprehensive benefits package that includes:
  • Employer-subsidized medical, dental, vision and life insurance
  • Short-term and long-term disability coverage
  • 401(k) match, flexible spending accounts and health savings accounts
  • Employee assistance program and educational assistance
  • Parental leave and 12 paid holidays
  • Paid time off for vacation, personal and sick time

Explore your benefits: https://honeywellaerospacebenefits.com/
The application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates. Job Posting Date: August 6, 2026
Sponsorship
We are currently not sponsoring applicants for work visas for this position. Applicants must be currently authorized to work in the United States on a full-time basis.
#LI-Hybrid
U.S. PERSON REQUIREMENTS
Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. person, which is defined as, a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status.

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About Honeywell

Sourced by ZipRecruiter

Honeywell is charging into the Industrial IoT revolution with the establishment of Honeywell Connected Enterprise (HCE), building on our heritage of invention and deep, on-the-ground industry expertise. HCE is the leading industrial disruptor, building and connecting software solutions to streamline and centralize the assets, people and processes that help our customers make smarter, more accurate business decisions. Moving at the speed of software, we are creating, innovating and delivering solutions fast, challenging the way things have always been done, piloting new ways for all of us to work, and expecting our successes to set new standards for our customers and for Honeywell. The Chief Architect for Honeywell Connected Enterprise will lead a team of architects and system engineers responsible for the design of applications and infrastructure that deliver high value outcomes for customers in industrial, buildings, distribution centers, and aerospace vertical markets. The Chief Architect will work directly with leadership, development teams, and offering management to design well integrated solutions that utilize software platforming to encourage reuse and speed to market.

Industry

Furniture manufacturing

Company size

10,000+ Employees

Headquarters location

Charlotte, NC, US

Year founded

1906

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