About the job
Information & Sensor Fusion Engineer
Preferred Location: Ottawa
Reports to: CTO
Type: Full-Time
About Dominion Dynamics
Dominion Dynamics is pioneering Canada’s first modern defense prime, focusing on software-defined, attritable, and sovereign solutions.
We envision a future where military strength relies not on singular, sophisticated platforms but on the agile coordination of AI-driven capabilities across all operational domains. Our systems are designed for deployment with operators, ensuring sovereignty and adherence to Canada's democratic principles.
Our operations prioritize speed and comply with Canadian law and obligations, particularly within the Canadian Armed Forces (CAF) community, especially in challenging environments like the Arctic.
Our founding team boasts expertise from prestigious organizations such as Anduril, Google, Amazon, and the Canadian Armed Forces, and we seek innovative thinkers who excel at the intersection of autonomy, aerospace, and national security.
Why This Role Matters
High-fidelity sensing and real-time perception are crucial for reliable autonomous operations in remote and contested environments. This position bridges the gap between experimental machine learning research and operational sensor fusion processes, enhancing situational awareness, robustness, and mission success.
The Role: Forward Deployed Software Engineer - Information & Sensor Fusion
You will spearhead the development and implementation of sensor-fusion and perception systems, engaging in model development, real-time inference, and integration with edge computing and data pipelines. This role emphasizes creating high-performance, testable fusion stacks that ensure reliable outputs in field conditions.
Note: This position involves a forward-deployed mandate, requiring you to work directly with deployed systems and operators in real-world scenarios.
What You’ll Do
Design, train, and validate sensor-fusion models and perception pipelines using Python and frameworks such as PyTorch or TensorFlow.
Implement real-time inference and optimization techniques suited for edge computing and resource-constrained hardware.
Develop perception pipelines that integrate Computer Vision (CV) with classical estimation methods, utilizing OpenCV and OpenGL/WebGL as required.

