{"id":42690,"url":"https://alion.io/job/aerovect-senior-software-engineer-simulation","title":"Senior Software Engineer, Simulation","company":{"id":4809,"name":"AeroVect","domain":"aerovect.com","url":"https://alion.io/company/aerovect","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":34,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["London, United Kingdom","Atlanta, United States","Toronto, Canada"],"countries":["GB","US","CA"],"hiring_countries":["US","GB","CA","IT","PL"],"hiring_countries_total":5,"salary":{"min":140000,"max":170000,"currency":"CAD","period":"year","gross":null,"usd_annual":119107},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CARLA","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Gazebo","optional":false},{"name":"Isaac Sim","optional":false},{"name":"Linux","optional":false},{"name":"NVIDIA Omniverse","optional":false},{"name":"Python","optional":false},{"name":"ROS","optional":false},{"name":"ROS2","optional":false}],"status":"live","first_seen_at":"2026-08-05T15:57:00Z","employer_posted_date":"2026-08-05","last_verified_at":"2026-10-17T22:42:07Z","board_verified":true,"closed_at":null,"days_open":73,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":73},"description":"Who We Are\nAeroVect 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.\nYou 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.\nYou Will\nBuild 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\n\nMaintain 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\n\nBuild and operate scenario execution in the cloud: orchestration, parallelism, result aggregation, artifact capture, and runtime cost modelling\n\nBuild 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\n\nBuild failure-triage tooling: failure clustering, per-failure recordings, and dashboards that autonomy engineers can use without assistance\n\nExtend the scenario authoring tooling used by the V&V team, across backend and frontend\n\nMaintain simulation foundations: determinism and reproducibility, pipeline performance, and extending coverage to additional maps and sites\n\nYou Have\nBachelor’s in Computer Science, Electrical Engineering, Robotics, or related field\n\nStrong Python, with a track record of maintainable code in a shared codebase\n\nHands-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\nWorking knowledge of simulation, modelling, and validation methodology, including how simulated results are used to support claims about real-world behaviour\n\nDocker and Linux, distributed-systems fundamentals, and experience with GPU-based simulation environments\nCI/CD and cloud execution, experience integrating automated test workflows into CI for end-to-end validation coverage\n\nExperience analyzing simulation output and telemetry to identify performance bottlenecks and failure modes\n\nDebugging and profiling skills suited to distributed, GPU-bound systems\n\nWe Prefer\nMaster’s in Computer Science, Robotics, or related field\n\nExperience designing and validating safety-critical systems in autonomous driving, aerospace, or robotics\n\nExperience developing or maintaining autonomous vehicle software stacks (ROS/ROS2)\n\nCloud-based simulation infrastructure and large-scale distributed test execution\n\nSensor modelling (camera, lidar, radar), environment generation, or perception ground-truth pipelines\n\nAutomated testing, continuous integration, and data-driven validation\n\nLog/bag re-simulation from recorded real-world data\n\nTest-signal quality work: flake reduction, determinism debugging, golden-output comparison\n\nSafety standards exposure: ISO 26262, ISO 21448 (SOTIF), UL4600, 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