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.
Are you passionate about building AI/ML/LLM-powered systems that help safeguard the authenticity & the integrity of a platform used by millions? Are you passionate about protecting users from fake reviews and deceptive behaviors?
Yelp’s Trust & Safety is seeking a Machine Learning Engineer with an infrastructure focus for the Spam team, the team behind Yelp’s review recommendation software. You’ll be shaping our ML infrastructure and, over time, contribute to core machine learning model development and experimentation.
Due to our work’s sensitivity, our team operates under strict internal non-disclosure agreements (NDA), collaborating only with select NDA partners. This role is focused on building and adapting ML infrastructure specifically for the Spam team, ensuring both privacy and compliance.
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.

• Design and build secure, compliant ML infrastructure and automation, adapting Yelp’s Core ML stack for our NDA protected environment.
• Develop and maintain ML and data pipelines (training, evaluation, deployment, monitoring) serving models that fight fraud and spam.
• Implement access controls and data handling protocols to safeguard sensitive data at all stages of the ML lifecycle.
• Collaborate closely within the Spam team and with NDA partner teams in Core ML and Security, aligning with best MLOps and privacy practices.
• Contribute to model development, prototyping, and experimentation as the role evolves and the team’s needs expand.