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Model Predictive Control Jobs in California (NOW HIRING)

Kodiak

Senior Autonomy Engineer

Mountain View, CA · On-site

$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 ...

Freeform

Principal Simulation Engineer

Hawthorne, CA · On-site

Contribute to the development of model-predictive control strategies alongside control engineers * Generate and maintain documentation for all algorithms, solvers, and integrated system Basic ...

Freeform

Simulation Engineer

Los Angeles, CA · On-site

Contribute to the development of model-predictive control strategies alongside control engineers * Generate and maintain documentation for all algorithms, solvers, and integrated systems Basic ...

Freeform

Senior Simulation Engineer

Los Angeles, CA

Contribute to the development of model-predictive control strategies alongside control engineers * Generate and maintain documentation for all algorithms, solvers, and integrated systems Basic ...

Freeform

Senior Simulation Engineer

Los Angeles, CA · On-site

Contribute to the development of model-predictive control strategies alongside control engineers * Generate and maintain documentation for all algorithms, solvers, and integrated systems Basic ...

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Showing results 41-60

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 Autonomy Engineer

Kodiak

Mountain View, CA • On-site

$123K - $169K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 21 days ago

Job description

This role demands deep technical expertise, mathematical rigor, and first-principles thinking to ensure safety in autonomous driving. You will own critical elements of our overall safety strategy to deliver safe, transformative autonomous trucking systems.

The Autonomy Safety team is charged with an incredibly challenging task: how to make - and prove - that an autonomous truck is incredibly safe (fewer than one fatal collision per 10 hours). There is no textbook for this problem. You will be part of a small, high-impact team working at the frontier of safety, leveraging existing methods and pioneering new ones when needed.

What You'll Do: 

  • Write C++ to efficiently search high-dimensional failure spaces at scale
  • Use first-principles analysis to develop requirements for safe perception, localization, prediction, motion planning, and control, collaborating directly with the Autonomy Software teams building those systems
  • Identify failure modes and edge cases that expose weaknesses in our system before they appear in on-road environments.
  • Perform hazard analysis, risk assessments, and fault tree analysis that feed directly into our safety case and drive system design decisions
  • Provide analysis to support complex autonomy system design trade-offs that affect safety and performance
  • Lead and support structured testing campaigns like simulation, track, on-road, and hardware-in-the-loop to validate that our safety claims hold
  • Pioneer new methods when existing ones aren't enough to hit our safety targets
  • Drive safety thinking cross-functionally - this team doesn't sit in a corner; it shapes how everyone builds

What you'll bring:

  • M.S. or Ph.D. in engineering, mathematics, statistics, or a related technical field
  • Deep understanding of kinematics, dynamics, and system modeling
  • Strong foundation in probability and statistics
  • Strong programming skills in C++ and/or Python
  • Hands-on work across one or more of the following:
    • Sensor fusion and object tracking (e.g., Kalman filtering, particle filters, least squares estimation)
    • Machine learning methods (e.g., neural networks, SVMs, kNN, regression models, decision trees)
    • Classical computer vision (e.g., edge/corner detection, camera calibration, optics)
    • Motion planning (e.g., A*, RRT, potential fields)
    • Control systems (e.g., PID, nonlinear control, model predictive control)
  • Excellent verbal and written communication skills, particularly on technical topics
  • Safety analysis methodologies: FTA, FMEA, HARA
  • Safety-critical standards: ISO 26262, DO-178, IEC 61508, SOTIF, UL 4600
  • Redundancy strategies and fault-tolerant system design

What we offer:

  • Competitive compensation package including equity and annual bonuses
  • Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and  MetLife (including a medical plan with infertility benefits)
  • MetLife Legal Services, Identity & Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, & Critical Illness Insurance
  • Flexible PTO, 10 paid holidays, and generous parental leave policies
  • Our office is centrally located in Mountain View, CA
  • Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging
  • Long Term Disability, Short Term Disability, Life Insurance
  • Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation) 
  • Fidelity 401(k)
  • Commuter, FSA, Dependent Care FSA, HSA
  • Various incentive programs (referral bonuses, patent bonuses, etc.)

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