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

General Motors

Robotics Controls Engineer

Sunnyvale, CA

$138K - $211K/yr

Experience with model predictive control, constrained optimization, or trajectory generation for real-world robotic systems. * Familiarity with ROS or similar robotics middleware. * Experience ...

1x

Software Engineer - Robotic Controls

San Carlos, CA

$54.75 - $69.75/hr

Proven ability to implement optimization-based whole-body control, EKF-based state estimation, or model predictive control for legged systems.Software Engineering: High proficiency in writing clean ...

1x

Software Engineer - Robotic Controls

San Carlos, CA

$54.75 - $69.75/hr

Proven ability to implement optimization-based whole-body control, EKF-based state estimation, or model predictive control for legged systems.Software Engineering: High proficiency in writing clean ...

Kodiak

Senior Autonomy Engineer

Mountain View, CA · On-site

$190K - $230K/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 ...

Nextracker

Autonomy Engineer

Fremont, CA

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 ...

Medium

Autonomy Engineer II - Robotics

Palo Alto, CA · On-site

$100 - $130/hr

Experience with trajectory optimization, model predictive control (MPC), or advanced planning algorithms. * Experience with NVIDIA Jetson, embedded Linux systems, or edge computing platforms.

Kodiak

Senior Autonomy Engineer

San Francisco, CA · On-site

$190K - $230K/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 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.

Robot Autonomy Engineer

Maven Robotics

San Francisco, CA • On-site

Full-time

Posted 3 days ago

New

Job description

Role Description

We are looking to recruit an exceptional Robot Autonomy Engineer to build the decision-making stack that turns a goal into coordinated, reliable robot behavior in real industrial applications - what the robot should do next, in what order, and how a fleet of them shares a workspace without getting in each other's way.

In this role you will:

  • Own the autonomy stack above the controller - task planning, behavior planning, path planning and trajectory planning - from the moment work arrives to the trajectories handed off to motion control.
  • Design the behavior architectures that structure long-horizon manipulation and navigation tasks, and that degrade into retry, recovery and operator handoff rather than into a stall.
  • Bring principled task planning to industrial workflows: goal and precedence reasoning, task allocation, and planning under uncertainty.
  • Plan and coordinate motion for multiple robots sharing an industrial facility - separation, reservation, deconfliction and deadlock-free repositioning - so that adding a robot adds throughput.
  • Integrate LLM and VLM reasoning into planning for task decomposition, subtask grounding and language-conditioned goals, together with the verification and fallbacks that make a model's output safe to execute on real hardware.
  • Define the contract between learned policies and classical planning: what the model may decide, what the planner must guarantee, and how the two hand off mid-task.
  • Interface with perception, intelligence, controls, simulation and platform software in designing functional architectures that hold up under real-world operation.
  • Hold the whole stack to measurable field performance - cycle time, success rate, intervention rate - through simulation, replay of recorded robot logs, and testing on real robots.
Qualifications

Must-have:

  • MS or PhD in robotics, engineering, mathematics, computer science or a related discipline.
  • Real-world experience in classical motion planning for one or more robots - search-based, sampling-based or optimization-based (A*, RRT/PRM, trajectory optimization, model predictive control) - carried onto hardware rather than left in simulation.
  • Real-world experience in behavior planning: finite state machines, behavior trees or comparable behavior architectures for long-horizon tasks, including failure detection and recovery.
  • Familiarity with task planning in the classical AI planning sense (STRIPS, PDDL, HTN) or decision-theoretic planning (MDP, POMDP), and the judgment to know when that machinery earns its complexity against a simpler reactive design.
  • Proficiency in Python and C++ programming, using up-to-date software development practices and tooling.
  • Self-starter attitude with strong ability to identify problems, prioritize them, then plan and execute working solutions.
  • Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.

Nice-to-have:

  • Practical experience fine-tuning and integrating LLMs or VLMs for task planning, including grounding model output in executable, verifiable plans.
  • Multi-robot coordination at fleet scale: task allocation and assignment, traffic management, deconfliction, multi-agent path finding.
  • Experience with mobile manipulation - coordinating a mobile base and one or more arms toward a single task.
  • Familiarity with ROS 2, and with fleet interface standards such as VDA5050.
  • Familiarity with planning and kinematics libraries such as Drake, OMPL or MoveIt.
  • Experience evaluating planners in simulation and against replayed field logs, and the regression testing that keeps a planner honest as it changes.
  • A track record of carrying autonomy from working demo to sustained field operation.
  • Familiarity with functional safety (FuSa) concepts.

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