Senior Software Engineer in Toronto

As a Senior Engineer, you will have the opportunity to work on challenging problems throughout our software stack, designing and developing robust and scalable solutions across embedded systems, cloud, and mobile. You’ll be collaborating with our multidisciplinary team to create an amazing product and solve one of the world’s most important problems.

Do you love solving complex problems, and want to see your work applied in real life? We are looking for people who are self-directed and driven by a desire for excellence as much as by curiosity and a desire to learn by solving previously unsolvable problems.

• Own the capture and inference pipeline. Multi-camera video from sensor to model to storage. No copies, no dropped frames, and it stays that way as resolution and model complexity go up.
• Make heavy vision models run in real time on hardware you can’t upgrade. Fixed power and thermal budget.
• Bring up cameras and sensors at the driver level.
• Turn sensor data into geometry. Fuse vision, position and motion into per-tree geolocation you can defend against surveyed ground truth.
• Make field problems reproducible at a desk. Replay of real scans, synthetic sources, on-device CI, regression tests against ground truth. Verify the fix before it ships.
• Identify problems before our customers do: frame drops, latency, thermal and power headroom, sensor health, storage. The camera must notice its own problems.
• Take our next generation platform from hardware definition to shipping product, help decide what that hardware should be.
• Ship to a remote fleet that’s online intermittently.
• 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.