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Model Predictive Control Jobs in California (NOW HIRING)

Kodiak

Autonomy Systems Software Engineer

San Francisco, CA · On-site

$203K - $241K/yr

Control systems (e.g., PID, nonlinear control, model predictive control) * Strong foundation in probability and statistics, including appropriate application of distributions (e.g., Gaussian, Poisson ...

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 ...

RoboForce

Senior Robotics Engineer, Manipulation

Milpitas, CA · On-site +1

$121K - $167K/yr

Control Theory: Practical experience implementing PID, Impedance Control, or Model Predictive Control (MPC) on real hardware. * Requires 5 days/week in-office collaboration with the teams. Bonus ...

RoboForce

Senior Robotics Engineer, Manipulation

Milpitas, CA · On-site

$121K - $167K/yr

Control Theory: Practical experience implementing PID, Impedance Control, or Model Predictive Control (MPC) on real hardware. * Requires 5 days/week in-office collaboration with the teams. Bonus ...

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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 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 are the typical challenges faced by engineers working with Model Predictive Control (MPC) 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 (MPC) 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 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:
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Model Predictive Control jobs near you
Senior Research Scientist, Multimodal Foundation Models and Robotics (Santa Clara)

Senior Research Scientist, Multimodal Foundation Models and Robotics (Santa Clara)

NVIDIA Corporation

Santa Clara, CA • On-site

$116K - $148K/yr

Full-time

Posted 3 days ago

Job description

We are now looking for a Senior Research Scientist focused on Multimodal Foundation Models and Robotics! NVIDIA is searching for an outstanding research scientist to build humanoid robot foundation models and systems in the Generalist Embodied Agent Research (GEAR) group. Everything that moves will eventually be autonomous. Our mission is to build general-purpose embodied agents that learn to explore and master complex skills across the virtual and the physical world.
You will work with an amazing and collaborative research team that consistently produces influential works on multimodal foundation models, large-scale robot learning, game AI, and physical simulation. Our past projects include Eureka , VIMA , Voyager , MineDojo , MimicPlay , Prismer , and more. One of our team's most recent milestones includes Project GR00T , a foundation model for humanoid robots. Your contributions will have a significant impact on our moonshot research projects and product roadmaps.
What you will be doing:
  • Design and implement novel AI algorithms and models for general-purpose humanoid robots and embodied agents;
  • Develop large-scale AI training and inference methods for foundation models;
  • Optimize and deploy AI models in physical simulation and on robot hardware;
  • Collaborate with research and engineering teams across all of NVIDIA to transfer research to products and services.

What we need to see:
  • A Ph.D. in Computer Science/Engineering, Electrical Engineering, etc., or equivalent research experience.
  • 5 years of relevant work/research experience across one or both of these fields:
    • Multimodal Foundation Models
      • Hands-on training experience and publications in at least one of the following topics: LLMs; Large vision-language models; Video generative models and diffusion algorithms; or Action-based transformers.
      • Outstanding engineering skills in rapid prototyping and model training frameworks (PyTorch, Jax, Tensorflow, etc.). Python is required; C++ and CUDA proficiencies are a big plus;
      • Excellent skills in working with large-scale machine learning/AI systems and compute infrastructure.
    • Robotics:
      • Hands-on training experience and publications in robot learning, such as reinforcement learning, imitation learning, classical control methods, etc.
      • Strong programming skills in Python, C++, ROS, and machine learning frameworks like PyTorch.
      • Deep understanding of robot kinematics, dynamics, and sensors;
      • Ability to safely operate robot hardware, lab equipment, and tools;
      • Knowledge of control methods, including PID, model predictive control, and whole-body control;
      • Familiarity with physics simulation frameworks such as MuJoCo and Isaac Sim;
      • Robot hardware design and hands-on building experience.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and productive people in the world. Please join us and be part of the forefront of developing general-purpose robots and embodied agents!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 299,000 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until November 9, 2025.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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