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Optimal Control Jobs (NOW HIRING)

Shift Robotics

Gait Control Engineer

Austin, TX · On-site

Model-based control (e.g., MPC, optimal control) * Extended Kalman Filters or nonlinear state estimation * RTOS-based embedded systems * ARM Cortex microcontrollers * Real-time signal processing

Shift Robotics

Gait Control Engineer

Austin, TX

Model-based control (e.g., MPC, optimal control) * Extended Kalman Filters or nonlinear state estimation * RTOS-based embedded systems * ARM Cortex microcontrollers * Real-time signal processing

Shift Robotics

Gait Control Engineer

Austin, TX · On-site

Model-based control (e.g., MPC, optimal control) * Extended Kalman Filters or nonlinear state estimation * RTOS-based embedded systems * ARM Cortex microcontrollers * Real-time signal processing

NEURA Robotics

$100 - $125/hr

Trajectory optimization and model-predictive control pipelines over robot state, contact schedules, ground reaction forces, centroidal momentum, and joint trajectories -- using reduced-order ...

Apple Inc.

$200 - $250/hr

Experience with model predictive control, optimal control, or reinforcement learning (sequential decision-making) * Experience working from raw logs or sensor data -- comfortable building analysis ...

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How much do optimal control jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for optimal control in the United States is $21.73, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $25.00 per hour, depending on experience, location, and employer.

What is optimal control?

Optimal control is a branch of mathematics and engineering that focuses on finding a control policy for a dynamic system so that a specific objective, such as minimizing cost or maximizing performance, is achieved. It involves determining the best way to influence a system's behavior over time, typically through the use of differential equations and optimization techniques. Applications of optimal control can be found in areas like robotics, aerospace, economics, and process engineering.

What are some common challenges faced by professionals working in optimal control, and how can these be addressed?

Professionals in Optimal Control often encounter challenges such as handling complex, high-dimensional systems, ensuring solutions remain computationally feasible, and balancing accuracy with real-time performance requirements. Collaboration with multidisciplinary teams—including system engineers, software developers, and data scientists—is essential to develop effective models and algorithms. Staying updated with the latest optimization techniques and leveraging advanced computational tools can help address these challenges, and many organizations support ongoing training or conference participation for career growth.

What are the key skills and qualifications needed to thrive as an optimal control engineer, and why are they important?

To excel as an Optimal Control Engineer, you need a strong background in control theory, applied mathematics, and engineering, often supported by a relevant degree such as electrical, mechanical, or aerospace engineering. Proficiency with tools like MATLAB, Simulink, and programming languages such as Python or C++, as well as familiarity with optimization algorithms, is essential. Analytical thinking, problem-solving, and effective communication are key soft skills for translating complex models into practical solutions. These skills are vital for designing and implementing efficient control systems that optimize performance and stability in real-world applications.

What is the difference between Optimal Control vs Control Systems Engineer?

AspectOptimal ControlControl Systems Engineer
Required CredentialsDegree in Control Engineering, Applied Mathematics, or related fields; often requires knowledge of optimization and algorithmsDegree in Electrical, Mechanical, or Control Engineering; focuses on designing and implementing control systems
Work EnvironmentResearch, algorithm development, mathematical modeling, often in academia or R&DDesign, testing, and deployment of control systems in manufacturing, automation, or robotics
Industry UsageUsed in aerospace, robotics, finance, and advanced automation for optimal decision-makingApplied across industries for real-time control of machinery and processes

Optimal Control focuses on developing mathematical algorithms to determine the best control strategies, often involving complex optimization techniques. Control Systems Engineers implement and maintain these control strategies in practical systems. While both roles require a strong background in control theory, Optimal Control emphasizes theoretical and algorithmic development, whereas Control Systems Engineering centers on practical application and system integration.

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What states have the most Optimal Control jobs?

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Optimal Control jobs near you
Infographic showing various Optimal Control job openings in the United States as of August 2026, with employment types broken down into 87% Full Time, and 13% Part Time. Highlights an 100% In-person job distribution, with an average salary of $45,201 per year, or $21.7 per hour.

Physical AI Architect

Motion Recruitment

Charlestown, MA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 5 hours ago

Job description

This client is developing solutions for high-throughput logistics using advanced AI, robotics, and real-time hardware integration. They are looking to hire a Physical AI Architect that can go onsite 4 days per week in Boston. In this position, you'll have the opportunity to shape the next generation of physical AI platforms. This role is a blend of technical depth and hands-on building. This is a full time position.
Required Skills & Experience
  • Advanced degree in Robotics, Computer Science, or similar, or equivalent industry accomplishment
  • Demonstrated experience bringing AI-enabled hardware solutions into production environments.
  • Deep knowledge of diffusion-based models for robotics and optimal control concepts
  • Hands-on expertise with diffusion policy learning, score-based generative modeling, MPC, trajectory optimization, and robust feedback control
  • Experience integrating sensors (RGB-D cameras, force/torque sensors, encoders), embedded computing (NVIDIA, Jetson, ARM SoCs, FPGAs), and robot actuators
  • Proficiency with Python and C++
  • Familiarity with modern robotics middleware (ROS 2 or similar)
What You Will Be Doing
Tech Breakdown
  • 40% Diffusion-based learning and model deployment
  • 30% Optimal control, trajectory optimization, and motion planning
  • 30% Hardware-software integration
Daily Responsibilities
  • 80% Hands On
  • 10% Management Duties
  • 10% Team Collaboration

The Offer
You will receive the following benefits:
  • Medical, Dental, and Vision Insurance
  • Paid Time Off
  • 401k


Applicants must be currently authorized to work in the US on a full-time basis now and in the future.

Apply