Machine Intelligence and Data AnalyticS team (MIDAS) is seeking a Principal/Staff Deep Learning Engineer to design and review the analytics infrastructure for applications and products based upon Android. We aim to optimize the user experience, including performance, battery-life, thermal, stability and reliability, for Oppo’s products by adopting data-driven methodology. In order to handle the ever increasing complexity of modern mobile systems, MIDAS investigates and creates novel computational, statistical, and decision-making methods. Our projects advance the state of the art of design, develop, and service quality in mobile technology.
Responsibilities:
Seek scientific solutions to highly ambiguous problems by crafting a technical vision and building consensus across teams.
Develop, evaluate, improve, optimize, and deploy our advanced models in mobile devices.
Solid understanding and hands on experience of various deep learning methods and feature engineering.
Identify benchmark datasets relevant to different industries and use-cases and work towards production-quality performance on them
Interface with global development teams on current and future data analytics architecture for products, applications, and services.
Define and develop guidelines, standards, and processes to ensure deliverables quality and integrity.
Requirements
PhD degree or MS degree with 8+ years of professional experience in Computer Science, Electrical Engineering, Statics, Mathematics or equivalent.
Strong mathematical foundation in machine learning and deep learning.
Track record of developing demanding deep learning algorithms, applications and neural networks.
Experience in developing deep learning algorithms for object detection and recognition, etc.
Experience with at least one of the deep learning frameworks such as TensorFlow, PyTorch, Keras, Caffe, and MXNet.
Strong programming skills in Python, Scala, R, JAVA, etc.
Preferred Qualifications:
Experience in deploying deep learning algorithms on mobile platforms
Experience in quantization/acceleration for deep learning models.
Publications in top tier international conferences and journals, such as CVPR/ECCV/ICCV/NeurIPS/ICLR/PAMI/IJCV, etc.