Summary

Yelp engineering culture is driven by our values: we’re a cooperative team that values individual authenticity and encourages creative solutions to problems. All new engineers deploy working code their first week, and we strive to broaden individual impact with support from managers, mentors, and teams. At the end of the day, we’re all about helping our users, growing as engineers, and having fun in a collaborative environment.

Yelp’s mission of connecting people with great local businesses requires the use of cutting-edge Machine Learning (ML) and Artificial Intelligence (AI) to scale across a vast and diverse base of users and businesses spanning various geographical locations. As an ML engineer, you will have the opportunity to foster these connections across millions of users and business listings using cutting-edge industry tools such as neural networks (NNs), large language models (LLMs), and traditional ML methods like XGBoost or linear models. You will be responsible for turning raw data into valuable signals and building the ML system end-to-end. This includes the full ML lifecycle from building data pipelines, training models, to deploying them in production, as well as deploying Gen AI applications.

This opportunity is fully remote and does not require you to be located in any particular area in Canada. We welcome applicants from throughout Canada. We’d love to have you apply, even if you don’t feel you meet every single requirement in this posting. At Yelp, we’re looking for great people, not just those who simply check off all the boxes.

What you'll do:

• Engage with diverse challenges such as personalizing ads, search ranking, Voice AI, AI chatbots, advertiser retention and churn prevention, data-driven storytelling, clickstream analytics, content type classification, delivering personalized recommended businesses to users, and sophisticated bot detection.
• Collaborate with cross functional teams, including software engineers, applied scientists, and product managers to identify and use the most relevant consumer and business data.
• Learn the fine art of balancing scale, latency, cost, and availability depending on the problem.

What it takes to succeed:

• Experience developing and productionizing machine learning models, including their supported data pipeline.Experience with machine learning using packages such as TensorFlow, PyTorch,
• Spark MLlib, XGBoost, Sklearn, etc.