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Model Predictive Control Jobs in Sacramento, CA (NOW HIRING)

Low-level path control (e.g., Model Predictive Control (MPC)) * Path planning algorithms such as A*, Dijkstra's algorithm, or similar * Multi-robot planning or coordination * Building sensor fusion ...

Low-level path control (e.g., Model Predictive Control (MPC)) * Path planning algorithms such as A*, Dijkstra's algorithm, or similar * Multi-robot planning or coordination * Building sensor fusion ...

Autonomy Engineer

Folsom, CA · On-site

$160 - $190/hr

Designing and implementing robot control and planning systems, including: + Low-level path control (e.g., Model Predictive Control (MPC)) + Path planning algorithms such as A\*, Dijkstra's algorithm ...

Build predictive and statistical models to forecast cost, schedule, risks, and performance metrics ... Strong knowledge of data management, business intelligence, document control, project controls, and ...

... systems, including predictive analytics, statistical process control, and automated data ... Advanced data analysis, statistical modeling, and decision‑making skills. * ASQ CQE, CSSBB, or ...

... predictive analytics, statistical process control, and automated data visualization tools ... Advanced data analysis, statistical modeling, anddecision‑makingskills. Preferred Experience

Minimum 5 years of hands-on experience building and deploying predictive models and machine ... Experience with MLOps/AIOps practices, version control tools (e.g., GitHub, Azure DevOps), Agile ...

New

Minimum 5 years of hands-on experience building and deploying predictive models and machine ... Experience with MLOps/AIOps practices, version control tools (e.g., GitHub, Azure DevOps), Agile ...

New

... systems, including predictive analytics, statistical process control, and automated data ... Advanced data analysis, statistical modeling, and decisionmaking skills. Preferred Experience * ASQ ...

Principal Quality Engineer

Roseville, CA · On-site

$135K - $175K/yr

... systems, including predictive analytics, statistical process control, and automated data ... Advanced data analysis, statistical modeling, and decision-making skills. Preferred Experience

Develop and maintain a comprehensive understanding of Novate's business models, revenue streams ... quality control, and ISO-compliant documentation Establish and maintain documented accounting ...

DAS / ERRCS Designer

Sacramento, CA · On-site

$35 - $45/hr

... modeling, heat mapping, link budget creation, and predictive coverage analysis using industry ... document control tasks such as printing, downloading, and filing. • Perform material and ...

DAS / ERRCS Designer

Sacramento, CA · On-site

$35 - $45/hr

... modeling, heat mapping, link budget creation, and predictive coverage analysis using industry ... document control tasks such as printing, downloading, and filing. • Perform material and ...

DAS / ERRCS Designer

Sacramento, CA · On-site

$35 - $45/hr

Conduct RF modeling, heat mapping, link budget creation, and predictive coverage analysis using ... Perform document control tasks such as printing, downloading, and filing. Perform material and ...

Controller

West Sacramento, CA · On-site

$90K - $140K/yr

... process control systems, information software, instrumentation, industrial networks, SCADA ... predictive insight and help lead and streamline initiatives that ensure company management. The ...

AI Solutions Engineering Delivery Lead

Sacramento, CA · On-site

$109K - $144K/yr

You will work on developing predictive models, conducting statistical analysis, and creating data ... control and continuous improvement of AI solutions, closely collaborating with business ...

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Model Predictive Control information

See Sacramento, CA salary details

$58.6K

$103K

$139.7K

How much do model predictive control jobs pay per year?

As of Aug 22, 2026, the average yearly pay for model predictive control in Sacramento, CA is $102,979.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,000.00 and $115,200.00 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.

What are popular job titles related to Model Predictive Control jobs in Sacramento, CA?

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

What job categories do people searching Model Predictive Control jobs in Sacramento, CA look for?

The top searched job categories for Model Predictive Control jobs in Sacramento, CA are:

What cities near Sacramento, CA are hiring for Model Predictive Control jobs?

Cities near Sacramento, CA with the most Model Predictive Control job openings:

Infographic showing various Model Predictive Control job openings in Sacramento, CA 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 $102,979 per year, or $49.5 per hour.

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 3 days ago


Job description

Job Description:

About Nextpower

Nextpower is a global leader in intelligent solar tracker and software solutions, optimizing solar power plant performance worldwide.

Within Nextpower, the Robotics and Autonomy team is building and deploying robotic systems that operate at scale in real solar environments. These systems combine perception, planning, control, embedded compute, and cloud infrastructure to increase inspection capacity, improve safety, and enable scalable operations.

We are seeking a Autonomy Engineer to help accelerate the next phase of that growth. This role focuses on advancing real-world autonomy performance across the navigation stack and enabling robots to operate more independently, more efficiently, and at greater scale.

This is a systems-level role spanning perception, planning, and control. You will work directly on robots, in the field and in test environments, to ensure the system performs reliably under real-world conditions.

Role Overview

We are scaling both the capability and deployment of our robotic systems. As autonomy improves, the system must support more robots per operator, operate with higher levels of autonomy across diverse environments, and maintain consistent performance as complexity increases.

This role is focused on building and improving a tightly integrated autonomy stack that works end-to-end on the robot. You will operate within constrained embedded systems where compute, bandwidth, and power are limited, requiring thoughtful tradeoffs in model selection, algorithm design, and system architecture. You will design and implement perception, planning, and control systems, validate them on real hardware, and continuously improve performance through testing, simulation, and data-driven iteration.

You are expected to own system behavior, not just individual algorithms. This means understanding how the full stack behaves in real environments, identifying gaps, and driving improvements that make the system more capable, reliable, and scalable.

Core areas of responsibility include:

  • Developing and improving autonomy capabilities across perception, planning, and control

  • Getting systems working on real robots, not just in simulation

  • Debugging and resolving issues observed in real-world operation

  • Building and using simulation, log replay, and data pipelines to accelerate iteration

  • Collaborating across hardware, integration, and test teams to ensure system-level performance

Minimum Qualifications

  • Bachelor's degree in Robotics, Computer Science, Electrical Engineering, or related field

  • 5+ years of experience in robotics, autonomy, or related fields, with demonstrated experience developing software for robotic or autonomous systems in real-world environments (not just simulation)

  • Strong proficiency in at least one of the following: C, C++, Python, or Rust

  • Strong experience with Linux command-line environments, Git, and Docker

  • Strong experience with ROS2-based systems

  • Strong problem-solving skills and ability to debug complex system behavior

  • Strong hands-on experience working with physical robotic systems

  • Coachable and able to operate effectively within a fast-moving, collaborative team

Preferred Qualifications

  • Developing and deploying perception systems such as:

    • Terrain classification
    • Object detection, recognition, and tracking
    • Semantic understanding of environments
    • Model training, evaluation, deployment, and inference optimization on constrained embedded systems (e.g., CUDA, TensorRT, ONNX Runtime)
  • Designing and implementing robot control and planning systems, including:

    • Low-level path control (e.g., Model Predictive Control (MPC))

    • Path planning algorithms such as A*, Dijkstra's algorithm, or similar

    • Multi-robot planning or coordination

  • Building sensor fusion, filtering, localization, and/or state estimation algorithms, such as:

    • Kalman Filters, Extended Kalman Filters (EKF)

    • Graph-based optimization methods

    • Characterizing sensor and motion noise

  • Working with simulation tools such as Gazebo or similar

  • Using log replay, data pipelines, and performance visualization to debug and improve systems

  • Applying CI/CD, release processes, and test-driven development in robotics systems

  • Debugging networking, remote systems, or communication stacks in deployed environments

  • Operating and deploying systems in outdoor, field robotics environments, including working under compute, bandwidth, and latency constraints

Join Us

At Nextpower, we are building robotic systems that operate at scale in real environments.

As a Senior Autonomy Engineer, you will play a critical role in advancing autonomy capabilities and enabling the next stage of system growth. This is an opportunity to work on real robots, solve real problems, and help scale a system that is already delivering value in the field.

Nextracker offers a comprehensive benefits package. We provide health care coverage, dental and vision, 401(K) participation including company matching, company paid holidays with unlimited paid time off, generous discretionary company bonuses, life and disability protection and more. Employees in certain positions may be eligible for stock compensation. All plans are in accordance with relevant plan documents. For more information on Nextracker's benefits please view our company website atwww.nextracker.com.

Pay is based on market location and may vary based on factors including experience, skills, education and other job-related reasons. The annual salary range for this position is 160K to 190K.

At Nextpower, we are driving the global energy transition with an integrated clean energy technology platform that combines intelligent structural, electrical, and digital solutions for utility-scale power plants. Our comprehensive portfolio enables faster project delivery, higher performance, and greater reliability, helping our customers capture the full value of solar power. Our talented worldwide teams are redefining how solar power plants are designed, built, and operated every day with smart technology, data-driven insights, and advanced automation. Together, we're building the foundation for the world's next generation of clean energy infrastructure.

Nextpower is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

We are Nextpower