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Optimal Control Jobs (NOW HIRING)

Draper

Principal Guidance & Control Engineer

Huntsville, AL · On-site +1

$95K - $245K/yr

Familiarity with trajectory optimization, optimal control, and similar techniques. Strong leadership and team management skills. Ability to motivate and develop a team of professionals. Excellent ...

Draper

Senior Software Autonomy Engineer

Cambridge, MA · On-site

$82K - $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

Principal Guidance & Control Engineer

Cambridge, MA · On-site +1

$95K - $245K/yr

Familiarity with trajectory optimization, optimal control, and similar techniques. Strong leadership and team management skills. Ability to motivate and develop a team of professionals. Excellent ...

Draper

Senior Software Autonomy Engineer

Cambridge, MA · On-site +1

$82K - $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 Navigation Analysis Engineer

Cambridge, MA · On-site +1

$82K - $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 ...

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How much do optimal control jobs pay per hour?

As of Aug 18, 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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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.

Sr. Robotics Engineer, Motion Planning

Contoro Robotics

Austin, TX • On-site

Full-time

Re-posted 8 days ago

Job description

Join Contoro Robotics - Revolutionizing Warehouse Automation with Cutting-Edge Robotics
At Contoro Robotics, we're on a mission to solve labor challenges through advanced robotic solutions. Headquartered in Austin, TX, our fast-growing startup is transforming the supply chain industry with our flagship warehouse automation technology. Our team is made up of top-tier experts in robotics, AI, and logistics, working together to push the boundaries of automation.
We're looking for talented and ambitious individuals to join us on this journey-helping shape the future of robotics while growing alongside a world-class team. If you're passionate about innovation, problem-solving, and making a real-world impact, we want to hear from you!
Senior Robotics Engineer, Motion Planning
Contoro Robotics is an Austin-based company building autonomous truck-unloading robots for warehouse operations. We deploy reliable, high-throughput robotic systems that handle heavy, unstructured freight in real logistics settings every day.
The Role
We are looking for a Senior Robotics Engineer to lead the next phase of motion-performance improvement for our robot arm. You will be the technical lead for motion planning and trajectory generation, owning how we turn planned motion into fast, smooth, dynamically feasible trajectories under real hardware constraints. Your focus is reducing cycle time while maintaining safety and payload stability, using strong fundamentals in kinematics, dynamics, constrained optimization, and hardware-backed performance validation. This is a senior individual-contributor role: you set technical direction for motion planning, own the hardest trajectory-optimization problems, and mentor other engineers, working closely with the autonomy team.
Responsibilities
Trajectory Optimization
  • Own trajectory generation and time parameterization: turn planned paths into time-optimal, dynamically feasible trajectories that minimize cycle time while respecting velocity, acceleration, jerk, and torque limits of the arm and payload.
  • Drive the next generation of our motion performance through advanced time parameterization, optimization-based motion (optimal control, QP/NLP), and payload-aware planning that meaningfully increases throughput.
  • Ensure smooth, jerk-limited motion that maintains payload stability and hardware safety, and robustly handle near-singularity and joint-limit edge cases without stalls or unsafe motion.

Path Generation and Planning Architecture
  • Own geometric path planning for a high-DOF manipulator moving boxes through cluttered, partially occluded container environments.
  • Evaluate and evolve the planning architecture and toolchain (e.g., MoveIt, OMPL, CHOMP/TrajOpt), making the build, adopt, and extend decisions for the planning stack.
  • Integrate perception outputs (container frame, box poses, occupancy) into the planning scene, reasoning about collision objects such as container walls, ceiling, and neighboring boxes.

Performance Validation
  • Define motion-performance metrics, benchmark alternative approaches, and validate gains both in simulation and on real hardware.
  • Use physics-based and kinematic simulation to develop and de-risk changes before they reach the fleet.

Technical Leadership
  • Set technical direction for motion planning and drive multi-quarter improvements in cycle time and reliability.
  • Mentor junior and mid-level engineers and raise the team's bar on motion-planning craft, testing, and reliability.
  • Collaborate across autonomy, perception, controls, and robot software to deliver end-to-end motion that is both fast and reliable across diverse box configurations.
Qualifications
Experience: 5+ years of professional experience in motion planning, trajectory optimization, or manipulator control, with production or real-hardware deployment and a track record of owning motion performance end-to-end.
Education: B.S. or M.S. in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field (or equivalent industry experience).
Technical Skills:
  • Strong grounding in classical trajectory generation and time-parameterization methods (e.g., TOTG/TOPP-RA, Ruckig, S-curve profiles), with the ability to derive, adapt, and implement them in production systems.
  • Deep grasp of manipulator kinematics and dynamics - forward/inverse kinematics, collision checking, velocity/acceleration/jerk and torque constraints, and singularity and joint-limit handling for 6/7-DOF arms.
  • Background in optimization-based motion (optimal control, QP/NLP-based trajectory optimization).
  • Deep proficiency in C++ (modern standards), Python, and ROS 1 or ROS 2.
  • Experience validating cycle-time and reliability improvements on real hardware, backed by simulation.

Soft Skills:
  • Strong problem-solving and data-driven decision-making; able to own ambiguous, high-impact problems and decompose them into actionable work.
  • Excellent communication - able to present complex technical concepts clearly to cross-functional teams and mentor other engineers.
Nice to Have
  • Hands-on expertise with MoveIt and motion planning frameworks (OMPL, RRT/RRT-Connect/PRM, CHOMP/TrajOpt).
  • Experience with industrial manipulators (e.g., KUKA) and real-time joint control.
  • Experience planning for multi-object or multi-pick manipulation.
  • Experience optimizing for throughput or cycle time in a production robotics or logistics setting.
  • Experience with GPU-accelerated or learning-based motion planning in addition to classical methods.
  • Experience with physics-based or kinematic simulation for planning validation (Isaac Sim, Gazebo, MuJoCo, Bullet).

*Recruitment Agencies: Please do not contact our employees regarding this role. We partner with a select group of approved recruiting firms and do not accept unsolicited outreach or candidate submissions.

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