AV Senior Engineer: Simulation and Testing

Why Aidoptation?

At Aidoptation, we work on the hardest problems in autonomous driving: high-speed, safety-critical scenarios where precision and robustness matter. Our technology is proven at racing speeds and translated into real-world highway autonomy. To succeed, we recruit the best engineers globally. We receive a high volume of applications and referrals and select for excellence and commitment alongside deep technical competence.

Role description

In this role, you will lead the development of the simulation infrastructure for autonomy and control systems, with ownership of the environments, tooling and workflows that enable high-confidence validation before vehicle deployment. You will build and maintain SIL and HIL simulation capabilities using VI-grade's software ecosystem including VI-CarRealTime, Vi-Drivesim and Vi-Worldsim and related tooling that support the full development loop, including ROS 2 integration, time synchronization, logging, replay and repeatable execution at scale. You will be responsible for ensuring the simulation stack is both technically rigorous and operationally dependable, so that engineers can use it as a core part of day-to-day development, regression testing and release readiness.

You will drive model fidelity in the areas that matter most for predicting vehicle behavior, including dynamics, actuation, delays, sensor characteristics and system failure modes. This role requires strong judgment on where additional realism materially improves engineering decisions and where simulation should remain efficient and scalable. You will develop scenario-based evaluation for highway driving and safety-critical manoeuvres, build automated parameter sweeps and regression pipelines and create tooling to measure agreement between simulation and real-world performance. A central part of the work is closing the sim-to-real loop by comparing simulation outputs against on-vehicle data, quantifying gaps with clear KPIs and dashboards and using those insights to continuously improve the stack.

You will work closely with autonomy, controls and vehicle integration teams to incorporate VI-grade's simulator, vehicle models, sensor simulation tools, and map representations into a unified validation workflow. Over time, you will help establish a simulation platform that accelerates iteration, improves release confidence, and enables large-scale evaluation of system behavior across a wide range of operating conditions. The outcome is a high-quality digital twin and evaluation pipeline that supports engineering velocity, strengthens production readiness, and provides credible validation evidence for partners, customers, and internal stakeholders. Occasional travel may be required to support test campaigns and vehicle validation.

Requirements

Must-Haves 

• MSc in Mechanical, Robotics, Computer Science, or similar, with 5+ years of equivalent industry experience, or a PhD with 3+ years of equivalent industry experience. 

• Strong simulation fundamentals: numerical methods, real-time constraints, and validation against real data. 
• Experience with VI-grade software, including VI-CarRealTime, VI-Drivesim and/or VI-Worldsim
• Familiarity with VI-grade's SIL/HIL simulation workflows and vehicle model integration

• Solid programming skills in Python and C++ with a strong focus on tooling, automation, and reproducibility. 

• Experience with robotics or AV simulation workflows (SIL/HIL, scenario-based testing, data replay). 

• Comfortable in Linux; ROS 2 experience is a strong plus. 

• Startup mentality: ship tools that unblock teams and improve iteration speed. 
• Fluent English (Dutch is a plus)
• Applicants must already have the right to work in the EU. Visa sponsorship is not available.

Nice-to-Haves 

• Experience with 3D simulation engines or environments (for example, CARLA, Isaac Sim, Gazebo, Unreal, or Omniverse). 

• Familiarity with OpenDRIVE/OpenSCENARIO/RoadRunner, map tooling, and scenario standards. 

• Exposure to CI/CD for simulation, containers, and large-scale evaluation on compute clusters.