
11 - 50 employés
📦 Logistique
💼 Conseil
🚀 Aérospatiale
💰 Seed Round en 2022-07
Logistics • Consulting • Aerospace
AeroVect est une entreprise technologique spécialisée dans l'automatisation avancée des équipements de soutien au sol (GSE) dans l'industrie de l'aviation. Le produit phare de la société, AeroVect Driver, est un système de conduite autonome hautement évolutif, compatible avec tous les fabricants, conçu pour améliorer la sécurité, la productivité et la durabilité des opérations de manutention au sol. Plébiscitée par les grandes compagnies aériennes et les prestataires de services au sol, AeroVect vise à révolutionner les opérations côté piste avec sa technologie de conduite autonome de premier plan, développée dans la Silicon Valley pour le marché mondial de l'aviation.
🕒 il y a 22 jours
🗣️🇺🇸🇬🇧 Anglais requis
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11 - 50 employés
📦 Logistique
💼 Conseil
🚀 Aérospatiale
💰 Seed Round en 2022-07
Logistics • Consulting • Aerospace
AeroVect est une entreprise technologique spécialisée dans l'automatisation avancée des équipements de soutien au sol (GSE) dans l'industrie de l'aviation. Le produit phare de la société, AeroVect Driver, est un système de conduite autonome hautement évolutif, compatible avec tous les fabricants, conçu pour améliorer la sécurité, la productivité et la durabilité des opérations de manutention au sol. Plébiscitée par les grandes compagnies aériennes et les prestataires de services au sol, AeroVect vise à révolutionner les opérations côté piste avec sa technologie de conduite autonome de premier plan, développée dans la Silicon Valley pour le marché mondial de l'aviation.
• Own and extend the simulation tooling • Build and extend the simulation environment, including production autonomy-stack integration, consumed interfaces, vehicle and actor models, and environmental and operational conditions • Maintain and improve integration as the autonomy stack evolves, including simulation-fidelity work • Build and operate cloud-based scenario execution, including orchestration, parallelism, result aggregation, artifact capture, and runtime cost modelling • Build and extend the evaluation layer with precise assertions and pass/fail criteria for release gating • Build failure-triage tooling, including failure clustering, per-failure recordings, and dashboards • Extend scenario-authoring tooling for the V&V team across backend and frontend • Maintain simulation foundations, including determinism, reproducibility, pipeline performance, and coverage across maps and sites • Support test quality through deterministic, reproducible testing and verification that each gate can fail
• Bachelor’s degree in Computer Science, Electrical Engineering, Robotics, or a related field • Strong Python skills and 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 proprietary simulators • Working knowledge of simulation, modelling, and validation methodology • Docker and Linux experience • Distributed-systems fundamentals • Experience with GPU-based simulation environments • CI/CD and cloud execution experience • 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 for distributed, GPU-bound systems • Preferred: Master’s degree in Computer Science, Robotics, or a related field • Preferred: experience with safety-critical systems in autonomous driving, aerospace, or robotics • Preferred: experience with autonomous vehicle software stacks such as ROS/ROS2 • Preferred: cloud-based simulation infrastructure and large-scale distributed test execution • Preferred: sensor modelling, 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, including flake reduction, determinism debugging, and golden-output comparison • Preferred: exposure to ISO 26262, ISO 21448 (SOTIF), UL4600, or ISO 13849 • Application form asks whether the candidate has simulation experience in robotics, automotive, or autonomous driving • Application form asks whether the candidate is located in North America or Europe • Application form asks whether the candidate will require sponsorship to work in the US or Europe
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