As an applied research scientist at Element AI, you will sit in a cross-functional team with several other people with a diversity of skill sets and you will enjoy a unique opportunity to use your creativity towards applying cutting edge machine learning methods to problems with significant impact on the users of our products.
You will be confronted with real-world challenges and datasets, and you will need to use your AI / ML expertise and creativity to apply existing methods and develop new ones to solve these problems in a practical and scalable way.
You will be called upon to assess possibilities and provide ideas and input into product design choices. This will involve understanding your customer (internal or external) needs and and translating them into solutions that address those needs.
You will be expected to both do the necessary research to propose appropriate models / techniques and to have the necessary expertise to implement and train the models yourself.
Where useful, you will not hesitate to employ classical machine learning methods, but you are enthusiastic at the idea of pushing the boundaries of deep learning and AI.
Finally, you will be expected to embrace the fact that the value of your work is ultimately reflected in the impact it has on the end-
customers using our products and to find ways of measuring that impact as an integral part of your mission.
You have significant understanding of the underlying theory of deep learning, operation research or related AI field. This expertise can come from extensive studies, previous industrial experience or awesome self-
taught projects you have done on a personal basis. You're also a solid programmer and you're comfortable doing scientific programming as well as product development and do not mind getting your hands dirty in various coding and engineering tasks.
This includes embracing modern devops principles to development and working in a setting where code is expected to be shared and peer-reviewed.
You understand that running a model on the varied and often noisy data that arises in a commercial context differs significantly from running it on a clean academic dataset, and that modifying a model or technique to work in that setting can be a significant and sometimes frustrating challenge.
You embrace this challenge and may even have previous experience tackling it.
You learn autonomously and will enthusiastically stay up to date in the literature and techniques of your field while participating in the various learning opportunities we offer.
Fully paid for benefits, flexible hours and participation in the employee stock option program.
The chance to work with one of Canada’s most prolific, passionate and dedicated community of ML & AI researchers, developers and scientists.
Lots of food, snacks and coffee.