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Optimal Control Jobs (NOW HIRING)

Draper

Senior Guidance & Control Engineer

Cambridge, MA · On-site

$82.30K - $220K/yr

... optimal control, and similar techniques. • Significant knowledge in the design and analysis of control systems. • Significant knowledge of design and implementation of trajectory design ...

Draper

Senior Guidance & Control Engineer

Cambridge, MA · On-site +1

$82.30K - $220K/yr

Familiarity with trajectory optimization, optimal control, and similar techniques. Significant knowledge in the design and analysis of control systems. Significant knowledge of design and ...

Draper

Senior Guidance & Control Engineer

Huntsville, AL · On-site +1

$82.30K - $220K/yr

Familiarity with trajectory optimization, optimal control, and similar techniques. Significant knowledge in the design and analysis of control systems. Significant knowledge of design and ...

Draper Labs

Senior Guidance & Control Engineer

Reston, VA · On-site +1

$82.30K - $220K/yr

Familiarity with trajectory optimization, optimal control, and similar techniques. * Significant knowledge in the design and analysis of control systems. * Significant knowledge of design and ...

Draper

Senior Guidance & Control Engineer

Reston, VA · On-site +1

$82.30K - $220K/yr

Familiarity with trajectory optimization, optimal control, and similar techniques. Significant knowledge in the design and analysis of control systems. Significant knowledge of design and ...

IntelliPro Group Inc.

Control engineer

Santa Clara, CA · On-site

$150K - $230K/yr

Strong expertise in control theory including nonlinear control, model predictive control, and optimal control * Experience with state estimation techniques such as Kalman filters, particle filters ...

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How much do optimal control jobs pay per hour?

As of Jun 4, 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 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 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 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 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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Optimal Control jobs near you
Infographic showing various Optimal Control job openings in the United States as of May 2026, with employment types broken down into 2% As Needed, 65% Full Time, 10% Part Time, 3% Temporary, and 20% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution, with an average salary of $45,201 per year, or $21.7 per hour.
Research Engineer - Control, Optimization & Safety Systems

Research Engineer - Control, Optimization & Safety Systems

ARM

Austin, TX • On-site

$198.10K - $268K/yr

Full-time

Posted 24 days ago

Job description

Overview
Arm is extending its "Arm everywhere" vision into robotics and embodied AI, where intelligence must operate under real-world constraints of latency, power, and safety. We are building a new robotics research lab in Austin focused on the science of integration-understanding how different paradigms (learning, control, planning) work together in real robotic systems, and how they map onto heterogeneous compute.
This role focuses on control, optimization, and safety as the foundation of deployable embodied AI. You will develop real-time control and safety-critical systems, and integrate them with learning-based components in hierarchical architectures. A key focus is ensuring that behavior can be bounded, verified, and enforced across the system.
Responsibilities
  • Develop control algorithms (e.g., MPC, optimal control) for complex robots
  • Integrate control with learning-based components in hierarchical systems
  • Design safety mechanisms (runtime assurance, constraint enforcement)
  • Contribute to real-time system design under strict latency constraints
  • Enable system-level integration across ML, perception, and systems
  • Translate safety and control requirements into compute and architecture insights

Required Skills and Experience
  • Strong background in control theory, robotics dynamics, optimization
  • Experience with real-time or safety-critical systems
  • Proficiency in C++ and/or Python
  • Experience with robotic platforms and simulators
  • Publications in top-tier venues

"Nice To Have" Skills and Experience
  • MPC, trajectory optimization
  • Control Barrier Functions or safety-constrained control
  • Formal methods and verification
  • Learning-based control
  • Experience with high-DOF or humanoid robots

In Return
You will help define how robotic systems are made safe, reliable, and controllable-shaping both Arm's research direction and the architectures that will power robots at global scale.
#LS-KS1
Salary Range:
$198,100-$268,000 per year
We value people as individuals and our dedication is to reward people competitively and equitably for the work they do and the skills and experience they bring to Arm. Salary is only one component of Arm's offering. The total reward package will be shared with candidates during the recruitment and selection process.
Accommodations at Arm
At Arm, we want to build extraordinary teams. If you need an adjustment or an accommodation during the recruitment process, please email accommodations@arm.com . To note, by sending us the requested information, you consent to its use by Arm to arrange for appropriate accommodations. All accommodation or adjustment requests will be treated with confidentiality, and information concerning these requests will only be disclosed as necessary to provide the accommodation. Although this is not an exhaustive list, examples of support include breaks between interviews, having documents read aloud, or office accessibility. Please email us about anything we can do to accommodate you during the recruitment process.
Hybrid Working at Arm
Arm's approach to hybrid working is designed to create a working environment that supports both high performance and personal wellbeing. We believe in bringing people together face to face to enable us to work at pace, whilst recognizing the value of flexibility. Within that framework, we empower groups/teams to determine their own hybrid working patterns, depending on the work and the team's needs. Details of what this means for each role will be shared upon application. In some cases, the flexibility we can offer is limited by local legal, regulatory, tax, or other considerations, and where this is the case, we will collaborate with you to find the best solution. Please talk to us to find out more about what this could look like for you.
Equal Opportunities at Arm
Arm is an equal opportunity employer, committed to providing an environment of mutual respect where equal opportunities are available to all applicants and colleagues. We are a diverse organization of dedicated and innovative individuals, and don't discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

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