Deep Learning Research Engineer Qualcomm
San Diego, CA

Job Description

Job Detail

Job IdE1967894Job TitleDeep Learning Research EngineerPost Date12/12/2018Company-DivisionQualcomm Technologies, Inc.-Corporate Research & Development

Job AreaEngineering - Systems

LocationCalifornia - San Diego

Job OverviewResearch minimal computation techniques for speed and power efficiencyCreate new Voice UI architectures for ubiquitous integration across mobile and mass-scale embedded devicesBayesian deep learning for mission-critical risk assessmentAdversarial learning for robustnessGeneralized equivariance for data efficiency and model robustnessReinforcement learning for behavioral planning and resource managementActive learning on massive datasets for training efficiencyTraining at the edge for personalization and privacyTools for optimizing on-device execution such as model compression and optimized compilation." id="hdnJobOverview" />Qualcomm Research is looking for world-class researchers in machine learning/deep learning. Come join a high-energy team of engineers building best-in-class solutions and tools needed to enable state-of-the-art networks to run on devices with limited power, memory, and computation. Led by world-renowned pioneering machine learning researcher, Max Welling, members of our team enjoy the opportunity to participate in cutting edge research while simultaneously contributing technology that will be deployed worldwide in our industry-leading devices. You will be part of a multi-disciplinary team that has repeatedly won the major deep learning competitions, such as the ImageNet large scale visual recognition challenge. Collaborate with cross-functional teams spanning hardware, software and systems. See your design in action on industry-leading chips embedded in the next generation of smartphones, autonomous vehicles, robotics, and IOT devices. The R&D work responsibility can include the development of new fundamental methods in the following areasResearch minimal computation techniques for speed and power efficiencyCreate new Voice UI architectures for ubiquitous integration across mobile and mass-scale embedded devicesBayesian deep learning for mission-critical risk assessmentAdversarial learning for robustnessGeneralized equivariance for data efficiency and model robustnessReinforcement learning for behavioral planning and resource managementActive learning on massive datasets for training efficiencyTraining at the edge for personalization and privacyTools for optimizing on-device execution such as model compression and optimized compilation.All Qualcomm employees are expected to actively support diversity on their teams, and in the Company.Minimum QualificationsBachelor's degree in Engineering, Information Systems, Computer Science, or related field.

2+ years Systems Engineering or related work experience.

" id="hdnMinimumQualifications" />Bachelor's degree in Engineering, Information Systems, Computer Science, or related field.

2+ years Systems Engineering or related work experience.

Preferred QualificationsExperience in deep networks (CNN, DBN, RNN, LSTM, DCN) or reinforcement learning (RL)Experience implementing DL/RL algorithms in high-level languages/packages (e.g. Python, C++, Java, MATLAB)Experience with classification and regression algorithms (e.g. SVM, MLP)Strong understanding of machine learning algorithms & principles, and numerical optimizationExperience using machine learning toolboxes (e.g. Caffe, TensorFlow, or PyTorch)Experience implementing machine learning algorithms on embedded platformsTrack record of research excellence and high quality publicationsKnowledge of application areas, such as computer vision or speech analysis " id="hdnPreferredQualifications" />Experience in deep networks (CNN, DBN, RNN, LSTM, DCN) or reinforcement learning (RL)Experience implementing DL/RL algorithms in high-level languages/packages (e.g. Python, C++, Java, MATLAB)Experience with classification and regression algorithms (e.g. SVM, MLP)Strong understanding of machine learning algorithms & principles, and numerical optimizationExperience using machine learning toolboxes (e.g. Caffe, TensorFlow, or PyTorch)Experience implementing machine learning algorithms on embedded platformsTrack record of research excellence and high quality publicationsKnowledge of application areas, such as computer vision or speech analysis Education RequirementsPreferred: Master's, Computer Engineering and/or Computer Science and/or Electrical Engineering" id="hdnEducationalRequirements" />Required: Bachelor's, Computer Engineering and/or Computer Science and/or Electrical Engineering

Preferred: Master's, Computer Engineering and/or Computer Science and/or Electrical EngineeringKeywords