We are looking for passionate and talented AI / Machine Learning scientists and engineers for our Toronto AI Lab to work closely with the global AI labs on Client and disruptive solutions that will shape out our global vision for Connected Home, Edge Computing, IoT and Robotics..
You will work on advanced research topics related to optimization and hyper-parameter optimization to make core Client and RL algorithms faster and more robust.
You will design Client experiments, invent new algorithms, and create prototype implementations focusing with applications to a variety of challenging business problems in areas from IoT, Connected Home and On Device Computing.
You will be encouraged to publish high quality papers and patents and collaborate with leading academic universities in this field.
You will be based in our new offices in downtown Toronto and work alongside a multi-disciplinary team that includes data scientists, Client / AI scientists, product managers, and software developers, to design and launch AI products and solutions that help predict, personalize and transform lifestyles of client'
s global footprint of devices and users.
Seniority will be commensurate with experience and accomplishments.
Principal Duties and Responsibilities :
Research and develop advances to core AI / Machine Learning algorithms. The research focus includes (but is not limited to) the following aspects :
Optimization and hyper-parameter optimization
Model adaptation and learning on the edge
Policy learning and adaptation in RL
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 Client 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 machine learning, optimization theory, or related areas.
Strong publication record in machine learning and deep learning at top conferences and journals.
A demonstrable track record of developing Client algorithms, solutions, and delivering / deploying prototypes / projects.
Experience with deep learning frameworks (e.g., Keras, Tensorflow, Tensorlite, MxNet).
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.
Last, but not least, a sense of ambition and passion to change the world using AI and machine Learning.