AI Research Intern focusing on advancing deep learning techniques in financial products at TD. Collaborating on large - scale datasets and representing the team at ML conferences.
About the role
- Intern AI Researcher at Analog Devices exploring AI models for efficient edge computing. Collaborating with experts to drive breakthroughs in model optimization and compression.
Responsibilities
- Explore new AI model architectures to run optimally on an edge compute with memory constraints.
- Drive breakthroughs by converting ANN models (such as CNNs and Transformers) to those that operate efficiently with reduced memory using paradigms like SNN, SSM, sparsity, or RNN
- Support proof of concept advancing model retraining, optimization, and compression
Requirements
- PhD Intern in electrical engineering, computer engineering, computer science, or a related field.
- Deep understanding in AI model architecture, ANN/SNN/ SSM /Sparsity
- Vast knowledge in Edge AI model Compression, Optimization, and Compilation
- Proficient with multiple frameworks, including Snntorch, PyTorch, TVM, TensorFlow, CUDA, C/C++, and Python
- Familiar with analog compute, in-memory compute, sparse compute, and neuromorphic systems.
- Strong analytical mindset with the ability to balance performance, power, and memory tradeoffs.
- Experience with the physical world, including vision, 3D point clouds, tactile, audio – Advantage
Benefits
- Various lunch-and-learn topics
- Social events with other interns and full-time employees
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