Senior Software Engineer (Embedded Vision Systems)
Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Sep 19, 2026.
Join our team as a Senior Software Engineer specializing in Embedded Vision Systems. In this role, you will tackle complex problems across our software stack, designing and developing robust and scalable solutions for embedded systems, cloud, and mobile. You will collaborate with a multidisciplinary team to create an innovative product that addresses one of the world's most pressing challenges. Your responsibilities will include owning the capture and inference pipeline, optimizing heavy vision models for real-time performance, and ensuring the reliability and observability of our systems.
Missions
- Conception et développement de solutions robustes et évolutives pour les systèmes embarqués, le cloud et les appareils mobiles.
- Responsabilité de la capture et du pipeline d'inférence, y compris la gestion de plusieurs caméras vidéo.
- Identification et résolution proactive des problèmes avant qu'ils n'affectent les clients, y compris la gestion des performances et de la qualité.
Profil recherché
- Experience with cameras and sensors at a low level. Image sensors, capture drivers, timing and synchronization, I2C/SPI/GPIO peripherals- Experience making a distributed or embedded system observable: metrics and logs from devices you can’t reach, dashboards and reports someone other than the author will use, and accuracy or quality tracked over time rather than measured once
- Comfort with Linux as an embedded platform: kernel customization and debugging, containers, cross-compilation
- Hands-on experience deploying vision models to edge devices
- Strong systems programming ability in a compiled language, such as C, C++, or rust, and real comfort at the boundary between application code, drivers and hardware
- Self-direction. This is a small, distributed team; the person in this role will often be the one who decides what “done” means
- Experience building real-time streaming media or vision pipelines. Buffer management, latency and throughput, backpressure, zero-copy memory, and the discipline to profile instead of guess
- Streaming or event-based frameworks, such as ROS or gstreamer, including writing custom elements
- And corresponding profiling and performance optimization techniques
- Embedded vision processors and neural network accelerators
- Kalman filtering, multi-object tracking, and geometric state estimation or photogrammetry
- Telemetry and observability pipelines, time-series metrics, and building dashboards or automated reporting on top of them
- Agriculture, robotics, autonomy, or any other domain where the physical world refuses to cooperate