Senior Software Engineer, Simulation

Role Description

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.

• 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.

Qualifications

• 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.

Requirements

• Master’s in Computer Science, Robotics, or related field (preferred).
• Experience designing and validating safety-critical systems in autonomous driving, aerospace, or robotics (preferred).
• Experience developing or maintaining autonomous vehicle software stacks (ROS/ROS2) (preferred).
• Cloud-based simulation infrastructure and large-scale distributed test execution (preferred).
• Sensor modelling (camera, lidar, radar), environment generation, or perception ground-truth pipelines (preferred).
• Automated testing, continuous integration, and data-driven validation (preferred).
• Log/bag re-simulation from recorded real-world data (preferred).
• Test-signal quality work: flake reduction, determinism debugging, golden-output comparison (preferred).
• Safety standards exposure: ISO 26262, ISO 21448 (SOTIF), UL4600, ISO 13849 (preferred).