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Embedded Artificial Intelligence Architect
MDA Space
Montréal, CAN
Full-time
Senior · 10+ years experience
Onsite
Discovered 3 weeks ago
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Role
The Embedded Artificial Intelligence Architect (Technical Lead) is responsible for the architecture, documentation, implementation, and training of complex AI/ML models for ground systems and embedded platforms, including those hosted on MDA’s AURORA™ software-defined satellites—enabling AI capabilities in space. The role will initially focus on ground-based development and validation, progressing to on-board deployment where it delivers clear mission value and ROI (e.g., network traffic prediction, anti-jamming mitigation, and cognitive radio applications). Initially, this position will serve as the single accountable architect for AI, with the goal of achieving an initial deployment this year or early next year.
Responsibilities
- Lead the design and delivery of embedded AI solutions for satellite systems.
- Provide technical leadership across the AI/ML lifecycle (concept, architecture, implementation, verification, and deployment), including mentoring and technical decision-making.
- Collaborate with AI/ML experts across MDA divisions to share best practices and accelerate development.
- Perform problem formulation and feasibility studies for AI/ML applications in telecommunication satellites.
- Define a phased roadmap from ground prototypes to on-board inference, prioritizing deployments based on mission impact, risk, and ROI.
- Refine requirements and select appropriate model architectures to meet mission needs within embedded compute, power, memory, and latency constraints.
- Develop and evaluate AI/ML approaches for network traffic prediction, anti-jamming mitigation, and cognitive radio functions, and translate results into deployable system capabilities.
- Assess and prototype suitable learning approaches—including offline training, online adaptation, and (where appropriate) reinforcement learning—while ensuring robustness and verifiability for operational use.
- Design and implement complex simulators (e.g., digital twins) to generate representative training data.
- Create, curate, and manage datasets used for training, validation, and testing.
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