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Model Predictive Control Jobs in California (NOW HIRING)

GRAM

Senior Software Engineer, Controls

San Francisco, CA · On-site

$150K - $250K/yr

Whole-body control, model predictive control, inverse dynamics, contact optimization, or legged robotics. * Real-time Linux, deterministic communications, actuator torque control, or safety-rated ...

Amazon

Applied Scientist, Navigation

Sunnyvale, 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 ...

Amazon

Applied Scientist, Navigation

Sunnyvale, CA

... 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 ...

Kodiak

Senior Autonomy Engineer

Mountain View, CA

$123K - $169K/yr

Control systems (e.g., PID, nonlinear control, model predictive control) * Excellent verbal and written communication skills, particularly on technical topics * Safety analysis methodologies: FTA ...

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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 1% As Needed, 71% Full Time, 22% Part Time, 5% Contract, and 1% Nights. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution.

Senior Software Engineer, Controls

San Francisco, CA • On-site

GRAM
See more jobs at GRAMexternal

$150K - $250K/yr

Full-time

Medical

Posted 7 days ago

Job description

The Mission

GRAM is a self replication company creating populations of insectoids for the physical economy.

Our first research frontier is self-preservation: the base case of physical self-replication. Our machines will survive, coordinate, and recover without humans. We believe scalable machine labor requires more than single-agent task generality or machines shaped in our image.

About the role

You will build the real-time control architecture that coordinates an insectoid's full body across changing geometry, orientation, contact, load, and machine state. The work spans dynamics, contact constraints, force allocation, trajectory tracking, actuator limits, transitions, disturbance rejection, and recovery, carried through production C++ and onto hardware. You will develop the control-oriented models, system-identification methods, and hardware-in-the-loop environments needed to build and validate the controller, then establish its measured operating envelope on the physical machine.

What you will do
  • Design whole-body controllers that coordinate motion and contact forces across coupled degrees of freedom.
  • Implement real-time control software in C++ with explicit timing, numerical stability, saturation, and fault behavior.
  • Develop control-oriented dynamics models, system-identification experiments, and actuator characterizations that improve controller fidelity.
  • Integrate state estimation, trajectory generation, manipulation, embedded systems, and safety logic into one testable control path.
  • Define quantitative envelopes for tracking error, contact stability, disturbance rejection, transition success, compute latency, and recovery.
  • Build controller simulations, hardware-in-the-loop tests, and physical-machine tests that reproduce failures and prevent regressions.
  • Use test evidence to distinguish controller defects from estimation error, unmodeled dynamics, actuator limits, and mechanical faults.
  • Document model validity and confirm simulated results on physical hardware.
Minimum qualifications
  • Bachelor's degree in mechanical engineering, electrical engineering, robotics, controls, applied mathematics, or a related field, or equivalent practical experience.
  • Demonstrated experience developing feedback control for a multi-degree-of-freedom robot, vehicle, aerospace platform, or other nonlinear physical system.
  • Strong C++ and Python skills and command of rigid-body dynamics, linear systems, optimization, and numerical methods.
  • Hands-on experience with a controls or dynamics stack such as Drake, Pinocchio, MuJoCo, ROS 2 control, Eigen, or an equivalent internal framework.
  • Deployed a controller on physical hardware and can present measured tracking, stability, latency, or disturbance-rejection results plus a failure that required redesign.
Preferred experience
  • Whole-body control, model predictive control, inverse dynamics, contact optimization, or legged robotics.
  • Real-time Linux, deterministic communications, actuator torque control, or safety-rated motion systems.
  • Controls deployed in aerospace, autonomous vehicles, industrial robotics, or field robotics.

The annual base salary range for this Palo Alto position is $150,000–$250,000. Health coverage, benefits, and generous equity come with the role. This role is on-site in Palo Alto. A relocation bonus is available. We hire to start as soon as possible. We review what you've shipped; if it's the caliber we seek, interviews take about a week, start to finish. We treat your work and conversations with discretion.

 

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