Deep Learning Researcher, Qualcomm

• Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience or PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field
• Experience in deep learning or reinforcement learning
• Ability to formulate research problems and design, experiment and implement solutions
• Programming experience in Python and hands-on experience with deep learning toolkits such as PyTorch, TensorFlow, Jax
• 6+ months of experience developing and/or optimizing machine learning models, systems, platforms, or methods

Desirable

• As Deep Learning Researcher at Qualcomm, you develop and quickly iterate on innovative research ideas, and prototype and implement them in collaboration with other researchers and software engineers
• You stay up to date with the latest research in the field and publish papers at scientific conferences
• You create impact and engage with other teams to see your ideas deployed on millions of devices, such as mobile phones, AR/VR headsets, or autonomous vehicles
• Our projects include the following research areas:
• Power efficiency: Neural network design, compression, quantization, algorithms, efficient hardware, and conditional computation
• Generative vision: Deep generative modeling using efficient architectures and algorithms for image and video generation
• Distributed & federated learning: Distributed training, continuous learning, model adaptation, and privacy-preserving federated learning
• Wireless AI: Machine learning design for wireless communication and sensing, wireless perception, learning on edge-device, inverse problems, weakly/semi-supervised learning
• Combinatorial optimization: Solving optimization problems using reinforcement learning and graph neural networks
• Causality and Interactive Learning: Causal representation learning, interactive causal discovery, (hierarchical) reinforcement learning & skill/affordance learning
• Efficient learning: Robust learning through minimal data, unsupervised learning, and on-device learning