Machine Learning Engineer
Granify is a rapidly growing technology company at the intersection of e-commerce, machine intelligence, and psychology. We’ve received investment from top investors, built a world-class team and a world-class product, and in the last year alone we generated over $650 million in incremental sales for many of the world's largest retailers!
We’re searching for an experienced Machine Learning Engineer who values mastery, authenticity, and positivity to help build and grow our product. As an engineer, your strong foundation in productionizing machine learning models will be essential in making long lasting improvements to our product. If you are a highly technical, hands-on, and mission-driven engineer, who has a passion for solving problems in the area of recommendation, search and e-commerce optimization then this is the role for you.
This is a full-time position. We are centrally located in Edmonton, but remote applications will also be considered.
- Research, design and prototype intelligent systems with the aim of enhancing online shopper experience.
- Oversee research prototypes and develop them into fully-fledged AI software that are ready to be delivered to our clients.
- Participate in active maintenance and code reviews in a large codebase, suggesting and implementing changes as appropriate.
- Keep up-to-date with the latest papers in artificial intelligence and machine learning to propose solutions for real problems in e-commerce.
- Build infrastructure to support the evolution of our shopper interaction toolset.
- Mentor other engineers, participate in code reviews, and share knowledge.
- Troubleshoot, test, and debug to your heart’s content.
- At least 2 years of real-world experience implementing Machine Learning software.
- Proficient in Python and/or C/C++, with an interest in learning new languages
- BSc (MSc or PhD preferred) in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering, Physics, or a related discipline, with (at minimum) graduate-level courses in machine learning, or equivalent practical experience.
- Strong research experience in machine learning, preferably in one or more of the following (in no particular order): reinforcement learning, natural language processing, recommendation and/or ranking systems, deep generative models, representation learning, AI interpretability, domain generalization, meta-learning, computer vision, deep neural network architectures.
- Proficient in deep learning frameworks like Tensorflow, PyTorch, etc. and scientific computing packages like NumPy. Able to implement an algorithm as described in an academic paper using these frameworks in quality code.
- Strong computer science background, with experience in object-oriented programming, systems design, data structures and algorithms.
- Familiarity with source control (Git) and Unix systems, including shell scripting.
- Good intuition for applying AI theory to make business-oriented products with minimal guidance.
- Communicate to introduce honesty and clarity (avoiding buzzwords and jargon) to experts in multiple disciplines. Demonstrate a mature understanding of the current possibilities and limitations of AI research.
- Curious, constantly looking for better ways to build things and excited to learn about emerging technologies.
- Online advertising and/or marketing analytics, behavioural targeting and/or web analytics.
- Working in an Agile software development environment.
- Using cloud solutions, preferably AWS.
- Distributed and/or parallel programming.
- An active GitHub repository.
You’ll work closely with an incredible group of the smartest, most interesting, genuinely good people around. You’ll work hard, learn quickly, and have plenty of excitement. You’ll also get a first hand view into the rapidly evolving, exciting intersection of e-commerce, machine intelligence and psychology.
With our continued growth as a company, you’ll find limitless opportunities for growth, development, and career progression.
Did you know? Granify is backed by early investors in Facebook, Uber, Twitter, Airbnb, Paypal, Pinterest, Palantir and Yelp. Wouldn’t it have been great to get in at one of those companies as they were taking off...