WHO WE ARE
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 http://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 WILL
- Build 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
YOU HAVE
- 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
WE PREFER
- 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