Embedded Artificial Intelligence Architect

MDA•Sainte-Anne-de-Bellevue, QC

1d•Onsite

About The Position

MDA Space is a global leader in robotics, satellite systems, and geointelligence, with a 55-year history of innovation and over 450 missions. We are a trusted mission partner to the global space industry, with over 4,000 experts across Canada, the US, and the UK. We enable highly skilled people to continually push boundaries and tackle big challenges, aiming to improve life on and above Earth. We are seeking an Embedded Artificial Intelligence Architect to join our Satellite Systems team in Montreal. This role is crucial for developing and deploying advanced AI/ML models for both ground systems and embedded platforms, including MDA’s AURORA™ software-defined satellites, enabling AI capabilities directly in space. The initial focus will be on ground-based development and validation, with a progression towards on-board deployment for applications like network traffic prediction, anti-jamming mitigation, and cognitive radio. This position will be the primary architect for AI initiatives, with a goal of initial deployment within the current or next year.

Requirements

M.Sc. in AI/ML, Computer Science, Computer Engineering, or a related field (or equivalent practical experience).
10 years experience in embedded software development.
Hands-on experience deploying or prototyping ML models on embedded targets (e.g., Raspberry Pi 5, Versal/FPGA-class devices).
Strong Python skills; experience with TensorFlow (embedded deployment) and/or PyTorch.
Ability to write production-quality code in C/C++.
Experience with version control and collaborative development workflows (e.g., Git).
Ability to work independently, prioritize effectively, and operate with minimal supervision.
Strong written and verbal communication skills in English and/or French.
Strong interpersonal skills and the ability to work effectively in large, cross-functional teams.
Disciplined, resourceful, and committed to high-quality engineering outcomes.

Nice To Haves

PhD in AI/ML, Computer Science or Computer Engineering
Experience with networking concepts, protocols, and architectures (e.g., DVB-S2X, 5G NTN, IP routing, SDN).
Knowledge of satellite communication standards (e.g., DVB-S2X).
CUDA experience and/or GPU optimization for model training or inference.
Experience with RF systems and signal processing fundamentals.
Experience using Jira and Confluence (or similar tools) to manage work and documentation.
Experience with embedded Linux.
Experience with disciplined software development practices (code reviews, testing, CI/CD).
Fluency in French.

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.
Define metrics and evaluate model performance, robustness, and resource utilization on target platforms.
Author and maintain technical documentation (architecture, design, training, verification, and deployment).
Partner with system engineers and software developers to integrate AI capabilities into the end-to-end solution.