ZipRecruiter

Log In

1

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

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 ...

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 ...

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 ...

previous pagenext page

Showing results 21-40

All JobsModel Predictive Control JobsModel Predictive Control Jobs in Sunnyvale, CA

Model Predictive Control information

See Sunnyvale, CA salary details

$64.6K

$113.3K

$153.7K

How much do model predictive control jobs pay per year?

As of Aug 22, 2026, the average yearly pay for model predictive control in Sunnyvale, CA is $113,344.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,000.00 and $126,800.00 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 job categories do people searching Model Predictive Control jobs in Sunnyvale, CA look for?

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

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

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

Model Predictive Control jobs near you
Infographic showing various Model Predictive Control job openings in Sunnyvale, 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 $113,344 per year, or $54.5 per hour.

Controls and Robot Learning Engineer

Bedrock Robotics

San Francisco, CA • On-site

$98K - $127K/yr

Full-time

Posted 4 days ago

Job description

Join the team bringing advanced autonomy to the built world
At Bedrock, we're moving AI out of the lab and into the real world. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we're deploying autonomous systems on heavy construction equipment across the country, improving safety on job sites and accelerating schedules on critical infrastructure projects.
We're not here debating the future of AI. We're deploying it in the real world. In just two years, we've raised $350M and achieved the first fully autonomous excavator deployments in construction.
This is where algorithms meet steel-toed boots. You'll work alongside construction veterans and world-class engineers to solve physical-world problems that simulations can't touch. If you're ready to do meaningful work on hard problems, we'd love to have you join us.
We are building our first fleet of autonomous construction machines and are seeking a Controls and Robot Learning Engineer. In this role, you will contribute to the development of crucial components of our onboard and offboard autonomy system. You will be responsible for creating models to be used for onboard controls, as well as analyzing, evaluating and simulating the system dynamics of complex, 100,000-pound construction robots.
What You'll Do
  • Onboard Control: Develop control laws for the base vehicle and automated arms, utilizing techniques such as MPC, Reinforcement Learning, linear and non linear control, computed torque, vehicle dynamics, and impedance control.
  • System Identification and Modeling: Build models that capture the state and control input propagation of complex construction robots like excavators. This involves a deep understanding of the direct and inverse geometry of robot arms (4 to 7 DOFs), vehicle dynamics, and overall system calibration.

What We're Looking For
  • 5+ years of professional engineering or research experience in control and real-time embedded systems
  • MSc or PhD in Computer Science or Robotics
  • Deep understanding of reinforcement learning, imitation learning, and optimization for dynamic systems
  • Strong programming skills (C++/Rust, Python)
  • Strong data analysis skills
  • Experience with safety-critical systems

Ways to Stand Out from the Crowd
  • Experience with machine learning training pipelines, especially reinforcement learning (RL) using learned or simulated plant models
  • Practical application of RL or model predictive control (MPC) for control algorithms in production autonomy environments
  • Experience working with pose estimation systems
  • Experience with controlling and modeling hydraulic systems

Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.

Apply