ZipRecruiter

Log In

1

Model Predictive Control Jobs in California (NOW HIRING)

Cypress HCM

Control Systems Engineer

Irvine, CA · On-site

$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 ...

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 ...

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 ...

Mind Robotics

Controls Engineer

Palo Alto, CA · On-site

$90 - $120/hr

Experience with whole‑body control, trajectory optimization, or model predictive control on legged or manipulator systems * Familiarity with field‑oriented control (FOC) or other motor control ...

next page

Showing results 1-20

All JobsModel Predictive Control JobsModel Predictive Control Jobs in California

Model Predictive Control information

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 California?

For Model Predictive Control jobs in California, the most frequently searched job titles are:

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
Infographic showing various Model Predictive Control job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Senior Research Scientist: Planning and Control

Holiday Robotics Inc.

Mountain View, CA • On-site

$200 - $250/hr

Other

Medical, Dental, Vision

Posted 4 days ago

Job description

Senior Research Scientist: Planning and ControlAbout the Role

Holiday Robotics Research Inc. is the US subsidiary of Holiday Robotics, the fastest-growing robotics company in South Korea. We build our systems entirely from scratch - hardware, firmware, low-level controllers, simulators, and end-user software - all in-house, with the goal of freeing humans from tedious, dangerous, and repetitive tasks.

We are looking for a Senior Research Scientist in planning and control to drive the development of motion planning, trajectory optimization, and navigation algorithms for our mobile-based humanoid robot, Friday. You will design computationally efficient, real-time algorithms that enable Friday to safely navigate dynamic, complex environments while executing dexterous manipulation tasks.

You will implement real-time, kinematically feasible motion planning; develop trajectory optimization and predictive control frameworks (e.g., MPC) that ensure dynamic balance under high payloads; and build robust pipelines for dynamic collision avoidance that fuse real-time perception to continuously re-plan on the fly. You will formulate algorithms that coordinate mobile-base navigation with upper-body bimanual manipulation for smooth, safe whole-body motion, validate them in our in-house simulator, and deploy them on physical hardware - working closely with the perception, control, machine learning, hardware, and firmware teams to respect real physical constraints such as actuator limits and communication latencies.

Key AreasRequired Qualifications
  • MS or Ph.D. in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a closely related quantitative field.
  • 7+ years of experience (or 5+ years with an advanced degree) in robotics, focusing on motion planning and control for mobile platforms, manipulators, or autonomous vehicles - both custom-built and commercially available systems.
  • Deep theoretical and practical understanding of classic and modern planning algorithms (e.g., GCS, RRT*, PRM), trajectory optimization (e.g., TrajOpt), and predictive control (e.g., MPPI).
  • Strong background in optimization theory and state estimation, with a deep commitment to mathematical exactness for the governing physical equations.
  • Proficiency in AI-assisted coding (e.g., Claude, Codex) to rapidly write, debug, and optimize code, accelerating the transition from research to real-robot deployment.
Preferred Qualifications
  • Experience with whole-body control architectures or model predictive control on mobile-based humanoid or quadrupedal platforms.
  • Experience with machine learning approaches to planning, such as imitation learning or reinforcement learning for trajectory generation and policy synthesis.
  • Experience in bimanual manipulation, including object handovers, dual-arm coordination, and impedance/admittance control.
  • Experience bridging the sim-to-real gap for complex, long-horizon tasks.
What We Offer
  • Base salary range: $200,000 – $250,000 USD annually, commensurate with experience and technical proficiency.
  • Competitive equity packages in a rapidly growing, well-funded startup.
  • 100% employer-covered health, dental, and vision insurance (full-network PPO).
  • Relocation stipend for out-of-state candidates.
#J-18808-Ljbffr

Apply