AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com.
You will own and extend the simulation tooling. A significant part of the role is test quality. A test suite that produces flaky failures does not provide usable information. Determinism, reproducibility, and verifying that each gate is capable of failing are ongoing responsibilities rather than one-off tasks.
You WillBuild and extend the capabilities of the simulation environment: integration with the production autonomy stack, the interfaces it consumes, vehicle and actor models, and the range of environmental and operational conditions that can be represented
Maintain and improve that integration as the autonomy stack evolves, including fidelity work where the difference between simulated and real inputs changes how the stack behaves
Build and operate scenario execution in the cloud: orchestration, parallelism, result aggregation, artifact capture, and runtime cost modelling
Build and extend the evaluation layer that turns a run into a verdict — assertions and pass/fail criteria precise enough to gate a release and stable enough to avoid false failures
Build failure-triage tooling: failure clustering, per-failure recordings, and dashboards that autonomy engineers can use without assistance
Extend the scenario authoring tooling used by the V&V team, across backend and frontend
Maintain simulation foundations: determinism and reproducibility, pipeline performance, and extending coverage to additional maps and sites
Bachelor’s in Computer Science, Electrical Engineering, Robotics, or related field
Strong Python, with a track record of maintainable code in a shared codebase
Hands-on simulation experience for autonomous systems, familiarity with simulation platforms such as CARLA, Applied Intuition, Foretellix, NVIDIA Omniverse, IsaacSim, Gazebo, or a proprietary in-house simulator Working knowledge of simulation, modelling, and validation methodology, including how simulated results are used to support claims about real-world behaviour
Docker and Linux, distributed-systems fundamentals, and experience with GPU-based simulation environments CI/CD and cloud execution, experience integrating automated test workflows into CI for end-to-end validation coverage
Experience analyzing simulation output and telemetry to identify performance bottlenecks and failure modes
Debugging and profiling skills suited to distributed, GPU-bound systems
Master’s in Computer Science, Robotics, or related field
Experience designing and validating safety-critical systems in autonomous driving, aerospace, or robotics
Experience developing or maintaining autonomous vehicle software stacks (ROS/ROS2)
Cloud-based simulation infrastructure and large-scale distributed test execution
Sensor modelling (camera, lidar, radar), environment generation, or perception ground-truth pipelines
Automated testing, continuous integration, and data-driven validation
Log/bag re-simulation from recorded real-world data
Test-signal quality work: flake reduction, determinism debugging, golden-output comparison
Safety standards exposure: ISO 26262, ISO 21448 (SOTIF), UL4600, ISO 13849
