tinyML (Systems) Internship Position at Qualcomm

 

We are hiring for 1 internship (Summer 2022) at Qualcomm AI Research - focus is on computer vision/machine learning, and embedded/low-power experience is a plus. 

 

Short description:

Looking for motivated and driven individuals skilled in low-power machine learning algorithms development and their hardware implementation. The team has a history of research and development of ultra-low power computer vision systems that are now commercially deployed. The person will work on innovative projects in the area of  embedded machine learning for vision & audio applications. The role covers a broad variety of tasks in the field on advanced machine learning with initial focus on the current project dealing with ultra-low power sensor systems and related hardware, algorithms and software. The person will be responsible for research, development and testing of machine learning algorithms and their implementation into prototype modules, with subsequent transfer to commercialization. It is expected that the individual will effectively collaborate with Qualcomm partners across different disciplines (systems, hardware, software, product management) from different groups/divisions/locations/industries. 

 

Please apply using the following link:

Intern requisition link (1 opening)

---

• Python

• Machine learning theory /deep learning / RL
• Model compression / quantization / optimization for embedded devices
• Neural Architecture Search / kernel optimization / run-time compilation
• On-device training / personalization / federated learning / NLP, Security and Privacy in Machine Learning
• Learned optimization, deep Generative Models (VAE, Normalizing-Flow, ARM)
• Audio and speech / NLP (e.g., BERT) / computer vision / wireless communication
• Proficiency with high-level languages/frameworks such as PyTorch, TensorFlow

• In study towards a Masters in Electrical Engineering, Computer Engineering, Computer Science, Data Science or Machine Learning 
• Must be available for 10 – 16 weeks during Summer 2022 (May-September) with a graduation date of December 2022 or later 

• Extensive experience in deep neural networks (e.g. CNN, RNN, Graph NN, Attention/Transformer, …) or deep reinforcement learning.
• Proficiency in designing, implementing and training DL/RL algorithms in high-level languages/frameworks (e.g. PyTorch, TensorFlow, Caffe). Designing the network for an embedded device is a plus.
• Track record of research excellence and high-quality publications (e.g. NeurIPS, CVPR, ICML, ICLR, ICCV, …).
• Expertise in at least one of the following fields: o Machine learning theory / deep learning / RL o Model compression / quantization / optimization for embedded devices o Neural Architecture Search / kernel optimization / run-time compilation
• On-device training / personalization / federated learning o Security and Privacy in Machine Learning
• Learned optimization o Deep Generative Models (VAE, Normalizing-Flow, ARM, etc)
• Audio and speech / NLP (e.g., BERT) / computer vision / wireless communication