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Model Predictive Control Jobs (NOW HIRING)

IntelliPro Group Inc.

Control engineer

Santa Clara, CA · On-site

Strong expertise in control theory including nonlinear control, model predictive control, and optimal control * Experience with state estimation techniques such as Kalman filters, particle filters ...

Optimal Inc.

Robotics System Developer

Warren, MI · On-site

Experience with model predictive control is a plus. Ideal Candidate Profile The ideal candidate is a well-rounded robotics engineer who can combine software, sensing, controls, and robot integration ...

Avride

C++ Software Engineer - Control

Austin, TX · On-site

In this role, you will architect sophisticated control systems using Model Predictive Control (MPC) and identify the dynamic parameters of our vehicles. What You'll Do * Design, implement, and ...

Avride

C++ Software Engineer - Control

Austin, TX · On-site

In this role, you will architect sophisticated control systems using Model Predictive Control (MPC) and identify the dynamic parameters of our vehicles. What You'll Do * Design, implement, and ...

IntelliPro Group Inc.

Control engineer

Santa Clara, CA · On-site

$150K - $230K/yr

Strong expertise in control theory including nonlinear control, model predictive control, and optimal control * Experience with state estimation techniques such as Kalman filters, particle filters ...

Optimal Inc.

Robotics System Developer

Warren, MI · On-site

Experience with model predictive control is a plus. Ideal Candidate Profile The ideal candidate is a well-rounded robotics engineer who can combine software, sensing, controls, and robot integration ...

Linde

Advanced Process Control Engineer

Tonawanda, NY · On-site

$69.75K - $102.30K/yr

Gain exposure to industrial model predictive control, fuzzy logic control, and real-time optimization technologies * Contribute to productivity and optimization initiatives What makes you great * You ...

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How much do model predictive control jobs pay per year?

As of Jun 2, 2026, the average yearly pay for model predictive control in the United States is $96,574.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,500.00 and $108,000.00 per year, depending on experience, location, and employer.

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 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 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 cities are hiring for Model Predictive Control jobs? Cities with the most Model Predictive Control job openings:
What states have the most Model Predictive Control jobs? States with the most job openings for Model Predictive Control jobs include:
What job categories do people searching Model Predictive Control jobs look for? The top searched job categories for Model Predictive Control jobs are:
Model Predictive Control jobs near you

Control engineer

IntelliPro Group Inc.

Santa Clara, CA • On-site

Full-time

Posted 12 days ago

Job description

Job Title: Control engineer
Position Type: Full time
Location: Santa Clara, CA, USA
Salary Range: $150,000 - $230, 000 (USD)
Job ID#: 158283
Job Description:
We are seeking talented Control Engineers to join our dynamic team and lead the development of state-of-the-art control and state estimation algorithms for our robot platform.
Responsibilities
  • Develop and implement state estimation, sensor fusion, planning, control algorithms that enable fast, dynamic and safe robot motion
  • Collaborate with cross-functional teams including embedded system, perception, hardware, AI
  • Optimize control performance across multiple domains including stability, safety, precision, and energy efficiency
  • Design and conduct experiments to validate control algorithms both in simulation and on hardware
  • Analyze system performance data to identify failure modes and improvement opportunities
  • Document technical approaches, implementation details, and experimental results
Requirements:
  • Master's or PhD in Robotics, Controls, Mechanical Engineering, or related technical field
  • 4+ years of professional experience developing control systems for dynamic robots
  • Strong expertise in control theory including nonlinear control, model predictive control, and optimal control
  • Experience with state estimation techniques such as Kalman filters, particle filters, and factor graphs
  • Proficiency in C++, Python, Rust for real-time robotics applications
  • Strong understanding of robot kinematics, dynamics, and mathematical modeling
  • Experience working with sensor integration including IMUs, encoders, force/torque sensors
  • Proven track record of implementing and testing control algorithms on physical robotic systems
  • Excellent problem-solving skills and ability to debug complex system interactions
Preferred Requirements:
  • Experience with highly dynamic control systems such as bipedal, quadruped, or humanoid robots
  • Knowledge of reinforcement learning or other machine learning approaches for control
  • Experience with whole-body control and contact dynamics for legged systems
  • Experience with real-time computing and optimization
  • Background in trajectory optimization and motion planning
  • Familiarity with ROS, simulation environments (e.g., Drake, Isaac Sim, SAPIEN, MuJoCo, PyBullet)
  • Track record of publications in top-tier robotics conferences/journals
About Us:
Founded in 2009, IntelliPro is a global leader in talent acquisition and HR solutions. Our commitment to delivering unparalleled service to clients, fostering employee growth, and building enduring partnerships sets us apart. We continue leading global talent solutions with a dynamic presence in over 160 countries, including the USA, China, Canada, Singapore, Japan, Philippines, UK, India, Netherlands, and the EU.
IntelliPro, a global leader connecting individuals with rewarding employment opportunities, is dedicated to understanding your career aspirations. As an Equal Opportunity Employer, IntelliPro values diversity and does not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, disability, or any other legally protected group status. Moreover, our Inclusivity Commitment emphasizes embracing candidates of all abilities and ensures that our hiring and interview processes accommodate the needs of all applicants. Learn more about our commitment to diversity and inclusivity at https://intelliprogroup.com/.
Compensation: The pay offered to a successful candidate will be determined by various factors, including education, work experience, location, job responsibilities, certifications, and more. Additionally, IntelliPro provides a comprehensive benefits package, all subject to eligibility.

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