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Model Predictive Control Jobs (NOW HIRING)

IntelliPro

Control Engineer (Santa Clara)

Santa Clara, CA · On-site

$170K - $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 ...

Avride

Lead Software Engineer - Control

Austin, TX

This role is roughly 80% hands-on engineering and 20% technical leadership - you'll spend most of your time architecting and shipping Model Predictive Control (MPC) systems and vehicle dynamics ...

Amazon

Applied Scientist, Navigation

San Francisco, CA · On-site

... as model predictive control (MPC)-based trajectory planning. You will develop navigation solutions that seamlessly blend data-driven intelligence with principled control-theoretic guarantees. Our ...

Avride

Lead Software Engineer - Control

Austin, TX · On-site

This role is roughly 80% hands-on engineering and 20% technical leadership - you'll spend most of your time architecting and shipping Model Predictive Control (MPC) systems and vehicle dynamics ...

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Model Predictive Control information

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$55K

$96.6K

$131K

How much do model predictive control jobs pay per year?

As of Aug 21, 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 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.
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Model Predictive Control jobs near you
Infographic showing various Model Predictive Control job openings in the United States 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 $96,574 per year, or $46.4 per hour.

Control Engineer (Santa Clara)

IntelliPro

Santa Clara, CA • On-site

$170K - $230K/yr

Full-time

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

Job description

Base pay range

$170,000.00/yr - $230,000.00/yr

Job Description

We are at the forefront of developing advanced robotic systems that solve real‑world challenges. We're building next‑generation robots designed to work alongside humans, operate in human environments, and help address growing labor shortages.

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
Bonus Qualifications
  • 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 stands as 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. With a dynamic presence in the USA, China, Canada, Singapore, Philippines, UK, India, Netherlands, and Germany, we continue to lead the way in global talent solutions.

IntelliPro is proud to be an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We also ensure that all applicants have access to accommodations throughout the hiring process. Learn more at https://intelliprogroup.com

Compensation

The compensation offered will depend on various factors, including location, experience, education, and job‑related skills. This role includes a competitive base salary, bonus, equity, and a comprehensive benefits package, subject to eligibility.

Seniority level
  • Mid‑Senior level
Employment type
  • Full‑time
Job function
  • Engineering and Information Technology
  • Technology, Information and Media and Robotics Engineering
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