Senior Control Systems Engineer – Vehicle Dynamics & Abstraction (m/f/d)

About This Opportunity

We are representing a confidential autonomous systems organization in München, Bayern, Germany, seeking a Senior Control Systems Engineer to lead the design and execution of robust control layers across a diverse fleet of autonomous vehicles. In this role, you will act as the critical boundary layer to the upper autonomy stack, ensuring that high-level motion commands are executed sensibly and strictly safeguarded at the physical limits of the vehicle.

Your designs will address complex, real-world scenarios rather than idealized environments. You will build control architectures that remain robust under severe, highly non-linear conditions including sudden tire slip, challenging terrain such as mud, gravel, potholes, and dense vegetation, steep inclines and side slopes, and asymmetric system degradation.

Responsibilities

• Control Architecture Abstraction: Maintain a standardized, platform-agnostic motion command interface that cleanly abstracts underlying mechanical complexity from the planning layers
• Multivariable Control & Allocation: Design and deploy control laws to manage path tracking, velocity execution, and actuator saturation, incorporating data-driven control allocation to dynamically distribute effort across multiple actuators
• System Identification & Modeling: Utilize data-driven system identification techniques to model unknown plant dynamics, latencies, and actuator responses
• Pragmatic Simulation & Verification: Validate control loops rapidly through lean, pragmatic subsystem simulations to iterate fast
• Motion Safeguarding: Ensure that incoming motion commands are dynamically evaluated, bounded, and safeguarded against the physical realities and constraints of the vehicle chassis in real time

What We're Looking For

• Proven professional experience developing and deploying real-time closed-loop control systems
• Deep foundational command of classical control theory, including frequency-domain loop-shaping, stability margin analysis (Bode/Nyquist), and standard state-space design methods (LQR/pole-placement)
• Demonstrated experience utilizing system identification methods or empirical frequency response estimation to characterize raw plant dynamics, alongside data-driven control allocation techniques
• Strong theoretical and practical foundation in Differential Geometry as applied to nonlinear systems, specifically utilizing Feedback Linearization and Differential Flatness for exact state transformation
• Proficiency in Kalman Filtering for state estimation under non-holonomic constraints
• High proficiency in MATLAB/Simulink, C++, or Python with ability to translate complex control logic into clean, deterministic execution ready for real-time environments

How We Work With You

Our talent team at Get A Job.ai has been exclusively retained to find the right professional for this senior-level position. When you apply through our platform, a dedicated recruiter will screen your background and technical experience. If there's a strong match, we will submit your profile directly to our client for consideration. Please do not attempt to contact the client directly, as all applications must come through our coordinated process to be considered.

Pay

Compensation details will be discussed during the screening process with our recruiting team.

Equal Employment Opportunity: Get A Job.ai is committed to inclusive hiring practices and works with clients who value diversity in their workforce. All qualified applicants receive consideration regardless of race, color, religion, sex, national origin, disability status, or any other characteristic protected by law.