Edge AI Intern, Summer 2026 at RBC · Orlando

Summary: RBC is leveraging Artificial Intelligence to transform the future of banking. As an Edge AI Intern, you will join a specialized team focused on moving high-performance AI models from the cloud to the edge, tackling challenges in model optimization and deployment on resource-constrained devices. Responsibilities: - Research and implement techniques to compress state-of-the-art models (LLMs, CNNs) for edge deployment, utilizing methods such as quantization (INT8, INT4), pruning, and knowledge distillation - Prototype and deploy inference engines on local hardware (e.g., mobile CPUs/NPUs, edge servers, or embedded systems) using frameworks like ONNX Runtime, TensorFlow Lite, or ExecuTorch - Analyze and profile model performance to identify bottlenecks; optimize inference pipelines for real-time financial applications (e.g., fraud detection, biometric authentication) - Experiment with split computing strategies to intelligently divide workload between the edge device and the cloud, balancing bandwidth constraints with computational power - Develop rigorous testing suites to measure power consumption, memory footprint, and inference speed across different hardware targets Required Qualifications: - Currently enrolled in a Master's program or advanced Undergraduate in Computer Science, Electrical Engineering, or a related field - Strong proficiency in Python and C++ (specifically for high-performance inference) - Deep understanding of deep learning frameworks (PyTorch or TensorFlow) and their internal mechanics - Experience with model compression techniques (Quantization, LoRA, etc.) - Familiarity with edge inference runtimes (e.g., ONNX, TensorRT, CoreML, or TFLite) Preferred Qualifications: - Research experience or publications in efficient deep learning or systems for ML - Experience with LLM inference optimization (e.g., vLLM, llama.cpp) - Knowledge of hardware-software co-design (understanding how memory hierarchy affects AI performance) - Previous exposure to the financial industry or privacy-preserving technologies (e.g., Federated Learning) Required Skills: Python, C++, Model compression techniques, Deep learning frameworks Important Skills: Edge inference runtimes Nice-to-Have Skills: Curious Mindset, Emerging Technologies, Innovation, Interpersonal Relationships, Personal Initiative, Taking Initiative, Teamwork Internship Start Date: Start in 2026 Summer Benefits: Mentorship: Direct access to RBC SA&I researchers and engineers who are deep experts in Edge AI architectures and modelling., Community: Participation in the RBC Student Program, including hackathons, executive networking, and technical "lunch and learns."