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Model Predictive Control Jobs in California (NOW HIRING)

Cypress HCM

Control Systems Engineer

Irvine, CA

$200K - $250K/yr

Lead the design and implementation of control algorithms for medical devices, including closed-loop systems, adaptive control, and model predictive control. * Develop simulation models (e.g., MATLAB ...

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 ...

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 ...

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 ...

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Model Predictive Control information

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.

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

Humanoid Robotic Engineer

Hyphen Connect Limited

San Francisco, CA

Other

Posted 9 days ago

Job description

We are searching for a Humanoid Robotic Engineer specialized in bipedal locomotion and manipulation. This role involves designing and implementing sophisticated control algorithms, with a focus on Reinforcement Learning, to enhance the performance of robotic systems both in simulation and real-world applications.

Responsibilities:

  • Develop and implement advanced control algorithms for bipedal locomotion and manipulation.
  • Train Reinforcement Learning (RL) policies in simulation and deploy them on physical hardware.
  • Conduct sim-to-real transfer and analyze gait/kinematics telemetry.

Required Skills:

  • Strong background in Model Predictive Control (MPC) and Whole-Body Control.
  • Extensive experience with robotic simulation environments (e.g., NVIDIA Isaac Sim, MuJoCo).
  • Familiarity with modern RL frameworks.

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