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Model Predictive Control Jobs in Chicago, IL (NOW HIRING)

... control * Follow model development best practices and model risk governance standards Business ... Familiarity with several predictive modeling techniques (e.g., GLMs, treebased models, gradient ...

Experience applying predictive modeling techniques to pricing, risk, product, or other complex business applications. * Experience with Git, GitLab, or other version control and collaborative ...

Demonstrated experience in machine learning, predictive modeling, or statistics / data mining using ... control, testing, and review practices Desired skills: Ability to work as part of a team and to ...

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Model Predictive Control information

See Chicago, IL salary details

$56.7K

$99.5K

$134.9K

How much do model predictive control jobs pay per year?

As of Sep 14, 2026, the average yearly pay for model predictive control in Chicago, IL is $99,485.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,000.00 and $111,300.00 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 job categories do people searching Model Predictive Control jobs in Chicago, IL look for?

The top searched job categories for Model Predictive Control jobs in Chicago, IL are:

What cities near Chicago, IL are hiring for Model Predictive Control jobs?

Cities near Chicago, IL with the most Model Predictive Control job openings:

Infographic showing various Model Predictive Control job openings in Chicago, IL as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $99,485 per year, or $47.8 per hour.

Principal Engineer - Digital AI, Consumer TIC

Northbrook, IL • On-site

UL Solutions
Professional, Scientific, and Technical Services • 10K+ employees

$155K - $175K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 9 days ago


UL Solutions rating

8.4

Company rating: 8.4 out of 10

Based on 28 frontline employees who took The Breakroom Quiz


Job description

Principal Engineer - Digital AI, Consumer TIC serves as the Global Subject Matter Expert for Digital AI technologies, enabling UL Solutions to develop standards, certification, assurance, training, advisory, and assessment services across digital AI systems. This role provides technical leadership for AI governance, enterprise and cloud AI, predictive and Generative AI, foundation and frontier models, AI agents and agentic workflows, multi-agent systems, and AI lifecycle assurance. The position translates emerging technologies, regulations, standards, and customer needs into scalable UL Solutions offerings that strengthen the company's AI standards and services portfolio.

Must Have

  • Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Computer Engineering, Information Systems, or a related technical discipline. Advanced degree preferred.

  • 15+ years of technical experience in software platforms, cloud systems, AI/ML systems, cybersecurity, technology risk, model lifecycle management, digital assurance, or related technology domains, including 5+ years of direct experience working with AI technologies.

  • Demonstrated experience translating AI governance, model risk management, and regulatory requirements into practical evaluation methodologies, controls, assurance frameworks, certification programs, or customer-facing services.

  • Demonstrated ability to evaluate and test AI systems and identify failure modes, risks, and control gaps, including model performance, transparency, traceability, explainability, reliability, robustness, human-in-the-loop controls, fairness and bias, privacy, security, accountability, and operational resilience.

  • Working knowledge of AI evaluation and testing methodologies, including benchmarking, scenario-based testing, red teaming, bias and fairness evaluation, robustness testing, model monitoring, and assessment of risk-mitigation controls.

  • Working knowledge of predictive AI and machine learning, Generative AI, Large Language Models (LLMs), foundation and frontier models, retrieval-augmented generation (RAG), AI agents, multi-agent systems, and associated technical, operational, and governance risks, including hallucinations, prompt injection, jailbreaks, data leakage, model drift, unsafe actions, and output reliability.

  • Strong presentation, technical writing, thought leadership, and stakeholder engagement capabilities.

    Preferred to Have

  • Advanced degree in Artificial Intelligence, Machine Learning, Robotics, Computer Science, Data Science, or a related technical discipline.

  • Familiarity with AI governance frameworks, standards, and emerging global AI regulatory requirements.

  • Experience developing or applying standards, conformity assessment methodologies, certification programs, audit frameworks, testing approaches, technical requirements, or commercial services for emerging technologies.

  • Demonstrated ability to influence customers, regulators, standards committees, industry forums, cross-functional business leaders, and technical stakeholders.

What we offer:

  • Total Rewards:  We understand compensation is an important factor as you consider the next step in your career. The estimated salary range for this position is $155,000-$175,000 and is based on multiple factors, including job-related knowledge/skills, experience, geographical location, as well as other factors. This position is eligible for annual bonus compensation with a target payout of 20% of the base salary. This position also provides health benefits such as medical, dental and vision; wellness benefits such as mental and financial health; and retirement savings (401K) commensurate with the standard rewards offered in each individual location or country. We also provide full-time employees with paid time off including vacation (15 days), holiday including floating holidays (12 days) and sick time off (72 hours). 

What UL Solutions employees say

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