Software Engineer, Data Backend

Summary

Yelp's engineering culture thrives on cooperative teamwork, valuing individual authenticity and encouraging creative problem-solving. New engineers deploy working code in their first week, and we support individual impact through managers, mentors, and teams. Ultimately, we focus on helping users, fostering engineer growth, and enjoying a collaborative environment.

Are you passionate about building AI- and ML-powered systems that protect millions of users from fraud, spam, and deceptive behaviors? As part of Yelp’s Trust & Safety Traffic Team, you'll play a key role in maintaining the authenticity and integrity of our platform by developing solutions that detect and neutralize threats from fake reviews, malicious traffic, and automated abuse.

In this role, you'll work with vast and ever-growing datasets, collaborating with cross-functional teams under disciplined privacy protocols. Your work will focus on building and scaling ML infrastructure and models that keep Yelp a trusted place for real people to connect with great local businesses. You'll have the opportunity to leverage cutting-edge technologies such as neural networks, large language models, and traditional ML techniques while keeping our systems secure and reliable in a continuously evolving adversarial environment.

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 as a Software Engineer, Data Backend

• Design, build, and maintain secure, compliant ML infrastructure and automation adapted for high-sensitivity environments.
• Develop and productionize machine learning and data pipelines serving real-time models that fight fraudulent traffic, spam, and bots.
• Extract valuable signals from massive datasets, using your expertise to turn raw data into actionable insights.
• Dive deep into data to uncover patterns indicative of suspicious or fraudulent behavior and iterate on detection signals.
• Drive adoption of best practices in MLOps (model versioning, CI/CD for ML, monitoring) and ensure robust privacy/data protection controls.
• Collaborate closely with teams across Yelp, including Core ML, Security, and Product, to proactively defend our users and business metrics.
• Provide technical mentorship and contribute to the continuous improvement of engineering standards and experimentation processes.

What It Takes To Succeed

• Solid foundation in software engineering and machine learning/data engineering best practices.
• Experience designing or adapting ML infrastructure, MLOps tooling, and data pipelines in Python (e.g., pandas, NumPy, scikit-learn, TensorFlow, XGBoost), Spark, AWS (e.g., S3, Redshift), and modern databases (e.g., SQL/NoSQL).
• Demonstrated ability to build large-scale, real-time distributed systems for detecting abuse, fraud, or adversarial behavior.
• Deep understanding of privacy, data protection, secure engineering, and access control within ML systems.
• Comfort working independently in a fast-paced, ambiguous environment, with the curiosity and tenacity to tackle evolving problems.
• Excellent communication skills and a collaborative mindset, especially when handling sensitive projects under NDA.
• Bonus: Experience with Kubernetes, MLflow, Kubeflow, Airflow, Scala, MapReduce, Flink, Cassandra, or related technologies.

What You'll Get

Based on the anticipated level of experience, the compensation range for this remote role in Canada is expected to be between $101,000 and $237,000 CAD. The actual compensation may vary based on the candidate’s experience and skill set.

Yelp offers comprehensive benefits. This role is posted to fill an existing position.

Key skills/competency

• Machine Learning
• Data Engineering
• MLOps
• Python
• AWS
• Spark
• Fraud Detection
• Distributed Systems
• Data Privacy
• Real-time Data Processing