Senior Vehicle Controls Engineer

About Wingfin

Democratising Autonomy through Deep Learning.

Wingfin develops autonomous driving systems. Our mission is rooted in accessibility: human-like autonomy for safe, cost-efficient, real-world scalability. By leveraging cutting-edge deep learning and end-to-end neural networks, Wingfin is redefining the landscape of self-driving technology. We are building a practical, cost-efficient platform that brings safe autonomy to every vehicle on the road. Headquartered in Singapore, the company boasts an international team of multi-talented experts. We are looking for talented and motivated individuals to join our team either in Singapore or Malaysia.

About the role

We are looking for a hands-on vehicle controls engineer to own the systems that convert a planned trajectory into safe, stable and comfortable steering, braking and acceleration commands on a real vehicle.

The successful candidate must have designed, implemented, tuned and tested control systems on physical vehicles. You should be comfortable moving between control theory, vehicle dynamics, software implementation, CAN signals, data analysis and in-vehicle testing. You will be responsible for making the vehicle follow its intended path and speed reliably across

different speeds, road geometries, vehicle loads and operating conditions.

What you will own

• Develop and maintain lateral and longitudinal vehicle-control systems, including steering, acceleration, braking, speed control and trajectory tracking.
• Translate planned paths and trajectories into actuator commands that account for vehicle dynamics, actuator limitations, delays, saturation and changing operating conditions.
• Select and implement appropriate control approaches, which may include feedforward and feedback control, gain scheduling, PID, LQR, MPC, nonlinear control or other suitable methods.
• Build or identify vehicle and actuator models using measurements from the real vehicle.
• Characterise steering, braking and throttle behaviour, including dead zones, hysteresis, rate limits, delays and nonlinearities.
• Integrate controllers with the vehicle through CAN or other drive-by-wire interfaces.
• Implement controllers in reliable real-time software and ensure predictable loop timing, latency and failure behaviour.
• Conduct simulation, hardware-in-the-loop, closed-course and on-vehicle testing.
• Instrument the vehicle, analyse logs and diagnose problems such as oscillation, overshoot, poor cornering, steering instability, uncomfortable braking or inconsistent actuator response.
• Define quantitative performance measures for tracking accuracy, stability, comfort and intervention rates.
• Develop regression tests so that controller changes can be evaluated safely and consistently.
• Implement safety limits, watchdogs, fault detection, degraded modes and safe fallback behaviour.
• Work closely with the planning, localisation, perception, embedded systems and vehicle integration teams.
• Document controller assumptions, operating limits, tuning decisions and test results.

What we are looking for

The most important qualification is evidence that you have successfully controlled a real vehicle.

You should be able to describe at least one vehicle-control system that you personally helped take

from design to physical deployment, including:

• The vehicle and actuator configuration.
• The control objective and vehicle model.
• The sensors and feedback signals used.
• The control method selected and why it was appropriate.
• Controller frequency, communication delays and actuator limitations.
• How the controller was tuned and validated.
• Problems encountered during vehicle testing and how they were resolved.
• Measured performance before and after your work.
• Safety mechanisms and failure behaviour.

You should also have:

• Strong practical understanding of feedback control, stability, dynamic systems and vehicle dynamics.
• Experience developing lateral or longitudinal controllers for cars, autonomous vehicles, mobile machinery or other comparable wheeled vehicles.
• Experience testing and tuning controllers on physical vehicles rather than only in simulation.
• Understanding of kinematic and dynamic vehicle models, tyre behaviour and the interaction between speed, curvature, steering and stability.
• Experience working with steering, throttle, braking or drive-by-wire systems.
• Experience with CAN bus, vehicle signals and actuator command interfaces.
• Ability to implement production-quality control software in C++, C or another suitable realtime language.
• Ability to use Python, MATLAB or equivalent tools for modelling, system identification, log analysis and controller evaluation.
• Strong debugging skills and a methodical approach to real-world testing.
• Ability to work directly with vehicle hardware and participate in track or road testing.

Useful additional experience

These are advantageous but are not mandatory:

• Passenger-car ADAS or autonomous-driving development.
• Electronic power steering, brake-by-wire, throttle control, ESC or vehicle motion-control systems.
• Model predictive control, optimal control, robust control or nonlinear control.
• State estimation, observers, sensor fusion or tyre-force estimation.
• Embedded Linux, RTOS or safety-critical real-time systems.
• Hardware-in-the-loop or vehicle-in-the-loop testing.
• Functional-safety practices, ISO 26262 or SOTIF.
• Openpilot, Autoware, Apollo, ROS, MATLAB/Simulink, CarSim, IPG CarMaker, dSPACE or similar platforms.
• Experience taking an experimental controller through repeated vehicle-testing and release cycles.

Demonstrated ability to make a real vehicle behave safely and predictably is more important than credentials or familiarity with a particular tool.

What success looks like

Within the first several months, you should be able to:

• Establish a clear baseline for the vehicle's current steering and speed-control performance.
• Identify the principal limitations in the vehicle, actuator interface and existing controller.
• Improve trajectory-tracking accuracy, stability and ride comfort on the physical vehicle.
• Introduce repeatable controller-evaluation and regression-testing procedures.
• Help us develop a well-understood, measurable and maintainable vehicle-control stack.