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Model Predictive Control Jobs in Boston, MA (NOW HIRING)

Motion Recruitment

Senior AI Architect

Charlestown, MA · On-site

... model predictive control (MPC), trajectory optimization, and feedback control systems · Experience architecting end-to-end robotic autonomy systems including perception, planning, and control · ...

STR

Lead System Engineer

Woburn, MA · On-site +1

$157K - $224K/yr

By leveraging expertise in machine learning, algorithms, model-predictive control, and software development we build tools that support tactical mission planning and execution, autonomous reasoning ...

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All JobsModel Predictive Control JobsModel Predictive Control Jobs in Boston, MA

Model Predictive Control information

See Boston, MA salary details

$59.8K

$104.9K

$142.3K

How much do model predictive control jobs pay per year?

As of Jul 7, 2026, the average yearly pay for model predictive control in Boston, MA is $104,918.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,700.00 and $117,300.00 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 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 Boston, MA? For Model Predictive Control jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Model Predictive Control jobs in Boston, MA look for? The top searched job categories for Model Predictive Control jobs in Boston, MA are:
What cities near Boston, MA are hiring for Model Predictive Control jobs? Cities near Boston, MA with the most Model Predictive Control job openings:
Model Predictive Control jobs near you
Infographic showing various Model Predictive Control job openings in Boston, MA as of July 2026, with employment types broken down into 96% Full Time, and 4% Contract. Highlights an 100% In-person job distribution, with an average salary of $104,918 per year, or $50.4 per hour.

Humanoid Robotic Engineer

Hyphen Connect Limited

Boston, MA • On-site

Other

Posted 13 days ago

Job description

We are searching for a Humanoid Robotic Engineer specialized in bipedal locomotion and manipulation. This role involves designing and implementing sophisticated control algorithms, with a focus on Reinforcement Learning, to enhance the performance of robotic systems both in simulation and real-world applications.

Responsibilities:

  • Develop and implement advanced control algorithms for bipedal locomotion and manipulation.
  • Train Reinforcement Learning (RL) policies in simulation and deploy them on physical hardware.
  • Conduct sim-to-real transfer and analyze gait/kinematics telemetry.

Required Skills:

  • Strong background in Model Predictive Control (MPC) and Whole-Body Control.
  • Extensive experience with robotic simulation environments (e.g., NVIDIA Isaac Sim, MuJoCo).
  • Familiarity with modern RL frameworks.

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