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

GRAM

Senior Software Engineer, Controls

Palo Alto, 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 ...

New

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 ...

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Showing results 21-40

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

Model Predictive Control information

See Fremont, CA salary details

$60.2K

$105.7K

$143.4K

How much do model predictive control jobs pay per year?

As of Sep 12, 2026, the average yearly pay for model predictive control in Fremont, CA is $105,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,400.00 and $118,200.00 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 Fremont, CA?

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

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

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

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

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

Model Predictive Control jobs near you
Infographic showing various Model Predictive Control job openings in Fremont, CA as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 18% Part Time, 4% Contract, and 1% Nights. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution, with an average salary of $105,716 per year, or $50.8 per hour.

Motion Planning and Controls Engineer

San Francisco, CA • On-site

Maven Robotics
See more jobs at Maven Roboticsexternal

$98K - $127K/yr

Full-time

Re-posted 4 days ago

Job description

Company Overview

Maven Robotics is building the world's leading general-purpose AI robots.

We are currently operating in stealth and are growing the world's best team in AI robotics. We are looking for self-starters that are the world's best in their field, who can innovate from a deep understanding of the fundamentals, and who share our values of unwavering truth seeking and integrity, humility, curiosity, and relentless determination.

Role Description

We are looking to recruit an exceptional Motion Planning & Controls Engineer to develop the software that brings our robot hardware to life, implementing planning and control algorithms that provide purposeful, performant, safe, reliable, and beautiful coordinated motion.

In this role you will:

  • Cover the entire software stack that governs robot motion, from optimization-based motion planning and control for an overactuated autonomous system, down to individual motor torque control (and everywhere in between).
  • Utilize deep expertise in and intuition for complex, high degree-of-freedom electromechanical systems.
  • Employ a true multi-disciplinary mindset, bridging mechanical, electrical, software, and systems engineering domains to solve real-world problems.
  • Interface with perception, intelligence, simulation, and platform software systems in designing thoughtful functional architectures that ensure world-class overall robot system performance.
  • Have an unrelenting drive to achieve exceptional motion quality via continual test, measurement, analysis, and improvement.
Qualifications

Must-have:

  • MS or PhD in engineering, mathematics, computer science or a related discipline.
  • Experience in optimization-based motion planning and control algorithms such as Model Predictive Control.
  • Experience in control, estimation, modeling, and analysis of complex. multi-degree-of-freedom electromechanical systems, including forward/inverse kinematics, forward/inverse dynamics, and task/joint space control.
  • Practical experience with motor servo control (position/velocity/torque control) architectures and tuning.
  • 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:

  • Familiarity with ground vehicle localization, mapping, and state estimation techniques (e.g. SLAM, Kalman filtering).
  • Familiarity with robotic simulation software frameworks/environments.
  • Familiarity with modern approaches to robotic manipulation, including state-of-the-art reinforcement learning (RL) and imitation learning (IL) approaches and their application to real-world robots.
  • Knowledge of brushless motor current control (e.g. field-oriented control).
  • Familiarity with functional safety (FuSa) concepts.

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