One of In-TAC’s employer partner is looking to push the boundaries of AI to build innovative product and service solutions to realize their global vision for a connected world. As a part of this initiative, they have set up an Advanced AI organization in North America centered in Silicon Valley. Further to this initiative, they 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.
You will work on advanced research to make core ML and RL algorithms faster, more accurate and more robust. You will design experiments, invent new algorithms, and create prototype implementations focusing on applications to a variety of challenging business problems in areas of IoT, Connected Home and On Device Computing. You will be encouraged to publish high quality papers and patents and collaborate with leading academia.
You will be based in our new offices in downtown Toronto and work alongside a multi-disciplinary team that includes ML/AI scientists, product managers and software developers to design and launch AI products and solutions that help predict, personalize and transform lifestyles of their global footprint of devices and users.
Seniority will be commensurate with experience and accomplishments.
Principal Duties and Responsibilities
- Research and develop advances to improve core AI/Machine Learning algorithms. The research focus includes (but is not limited to) the following:
- Network optimization, parameter and hyper-parameter optimization, architecture search, network compression
- Model adaptation and learning on the edge
- Policy learning and adaptation in RL
- Bayesian machine learning
- Semi-supervised and unsupervised learning
- Distributed learning
- Read, understand, implement, and improve state-of-the-art papers in the above topics
- Take ownership of projects and build proof-of-concepts (POCs)
- Actively participate in the research and academic community by disseminating novel results in top conferences and journals
- Stay up-to-date on developments in AI technologies and propose long term research plans
- 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 novel 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