Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Oct 8, 2026.
Join our Vehicle Motion Planning & Control team as a Software Engineer. You will be responsible for behavior and trajectory planning for our autonomous vehicle, as well as vehicle control and on-vehicle integration. Your role will involve algorithm design, real-time C++ implementation, simulation, tuning, and on-vehicle validation. You should have a strong background in software engineering, robotics or controls, and a desire to see your code move a physical vehicle.
Missions
- Conception et développement d'algorithmes de planification de trajectoire et de prise de décision pour des mouvements sûrs et fluides.
- Architecture, mise en œuvre et validation d'algorithmes de contrôle et d'estimation pour la dynamique longitudinale et latérale du véhicule.
- Collaboration avec les équipes de perception et de plateforme pour définir des interfaces claires entre la sortie de perception, la planification et le contrôle.
Profil recherché
- Hands-on experience in motion planning, decision making, or vehicle controls (e.g., optimization-based planning, search methods, optimal control, MPC, probabilistic decision making) on autonomous vehicles, ADAS, or robotic systems- Somebody with solid opinions based on experience, yet open-minded and flexible to find the best solution for the situation
- Experience taking features from design through real-world deployment - not just prototypes
- 3+ years of software engineering experience, with production-quality coding skills in modern C++ and Python
- Ability to work from the office in Paris, at least Monday, Tuesday and Thursday
- Ability to break down complex, ambiguous problems into well-defined technical solutions
- Solid foundation in linear algebra, geometry, statistics & probability, and vehicle dynamics
- Experience deploying planning or control software on real vehicles (AV, ADAS features like ACC, AEB, lane keeping / lane changing)
- Experience with real-time and embedded systems and RTOS
- Experience with ML-based approaches to planning (imitation learning, RL, learned cost functions) alongside classical methods

