AI/Software Developer Co-op

Job Summary:

We are looking for a technically strong and innovative co-op student to join our AI engineering team focused on advancing our AI-driven NTN (Non-Terrestrial Network) and TN (Terrestrial Network) wireless communication stack. This position offers hands-on experience at the intersection of AI, network optimization, and next-generation wireless technologies

You'll work with engineers and researchers developing the AI software foundation for intelligent network management, resource allocation, and signal processing across integrated satellite and terrestrial systems.

What You'll Gain

• Exposure to cutting-edge AI systems and real-world software development practices.

• Mentorship from experienced engineers and data scientists.

• Opportunity to contribute to open-source or internal projects used in production.

• Publishing a paper with your name and possibly file for patents

• A collaborative, supportive environment focused on technical growth and innovation.

Job Responsibilities:

• Develop and integrate AI components within the NTN/TN software stack to enable intelligent control, adaptive routing, and predictive optimization.

• Build machine learning models for dynamic spectrum allocation, beamforming optimization, and channel estimation.

• Create data processing pipelines for real-time network telemetry, traffic patterns, and signal metrics.

• Assist in training and evaluating AI models using simulation data or live network traces..

• Work collaboratively with cross-domain teams in AI, wireless engineering, and system architecture.

• Contribute to documenting architectures and AI workflows used within our networking platforms.

Required Skills:

• Enrolled in a Bachelor's or Master's program in Computer Engineering, Computer Science, Telecommunications, or related field.

• Solid programming skills in Python (for AI development)

• Understanding of machine learning fundamentals and neural network concepts.

• Experience with one or more ML frameworks such as TensorFlow or PyTorch.

• Familiarity with containerization (Docker), cloud environments (AWS), or CI/CD workflows is a plus.

• Understanding of machine learning applications in wireless networks (reinforcement learning, optimization, anomaly detection).

• Knowledge of simulation tools like ns-3, MATLAB is an asset.

• Knowledge of wireless communication principles, such as modulation, coding, and channel modeling.

• Familiarity with 5G/6G architectures, network slicing, and hybrid NTN/TN scenarios.

• Excellent analytical and collaboration skills; passion for research and innovation in AI-driven wireless systems.