Santa Clara, CA
Industry: Retail & Consumer Goods•
Not Specified years
Posted 152 days ago
As part of LG’s North America AI initiative, we are looking for passionate and talented AI / Machine Learning scientists and engineers with experience in reinforcement learning. A background and hands-on experience on building ML/RL algorithms with constraints imposed by the edge-computing paradigm such as privacy preservation, resource limitations, decentralized/distributed computation etc. is highly desirable.
You will work on advanced research topics and challenging business problems at the intersection of machine learning and Connected Home, IoT and Robotics. You will design novel experiments, invent new algorithms, and create prototype implementations focusing on the research gaps in privacy preserving ML/RL algorithms under resource constraints. You are also encouraged to publish high quality papers and patents and collaborate with leading academic universities in this field.
You will be based in Silicon Valley and work alongside a multi-disciplinary team that includes data scientists, ML/AI scientists, product managers, and software developers, to design and launch AI products and solutions that help predict, personalize and transform lifestyles of LG’s global footprint of devices and users.
Seniority will be commensurate with experience and accomplishments.
Principal Duties and Responsibilities:
Research and develop advanced Reinforcement Learning based solutions for edge-devices. The research focus includes (but is not limited to) the following aspects:
Deep Reinforcement Learning. Technology Focus: Mobile edge computing as well as security.
Distributed Reinforcement Learning Algorithms. Technology Focus: Learning on resource constrained edge devices.
Hierarchical Deep Reinforcement Learning. Technology Focus: Self-learning systems.
Read, understand, implement, improve, and explain state-of-the-art papers in the above topics.
Take ownership of projects and build proof-of-concepts (POCs) that can demonstrate utilization, value, and lead to scalable solutions.
Actively participate in the research and academic community by disseminating novel results in top conferences and journals.
Stay up-to-date on developments in the field and propose long term capability buildup.
PhD in Computer Science, Electrical Engineering, Statistics or related quantitative discipline with a focus on reinforcement learning, machine learning, optimization theory or related areas.
Strong publication record in top conferences and journals.
A demonstrable track record of developing novel algorithms, solutions, and delivering/deploying projects.
Software engineering experience in two or more of C/C++, Python, Scala, Java, R, Matlab.
Experience working with edge-computing frameworks like, CoreML, Greengrass etc. preferred.
Experience with deep learning frameworks (e.g., Keras, Tensorflow, Tensorlite, MxNet) is desirable.
Experience with chipset architectures (Snapdragon 845, Google edge TPUs etc.) is desirable.
Last, but not least, a sense of ambition and passion to change the world using AI and machine Learning.