Machine Learning Engineer II, Core Engineering

Pinterest, Inc.

Job Overview

Location

Toronto, ON, CA

Job Type

Full-time

Full Job Description

📋 Description

• Machine Learning Engineer II, Core Engineering at Pinterest, Inc. in Toronto, ON, CA – a role focused on building cutting-edge machine learning systems that power personalized experiences for over 500 million global users, directly contributing to Pinterest’s mission of inspiring users to create a life they love.
• Day-to-day responsibilities include developing and deploying deep learning and ML models for personalization across key product surfaces such as Homefeed, Ads, Growth, Shopping, and Search; partnering with cross-functional teams to experiment and improve ML models; building scalable data processing pipelines using big data technologies like Hadoop and Spark; leveraging unique data properties to enhance candidate retrieval; and staying current with industry trends in recommendation systems through rapid experimentation and product launches.
• Pinterest is a mission-driven company with over 4,000 global employees, where AI is deeply integrated into the product experience—not just as a feature but as a core partner in creativity and impact. The engineering teams are small, agile, and growing, offering hands-on access to one of the largest and most unique datasets in the industry, enabling engineers to work on large-scale recommendation systems at a scale rarely found elsewhere.
• In this role, you will deepen your expertise in applied machine learning, gain end-to-end experience productionizing ML systems at scale, collaborate with world-class engineers and product teams, and contribute directly to user-facing innovations that affect hundreds of millions of people. You’ll also sharpen your ability to communicate technical approaches clearly—a key part of Pinterest’s AI-informed hiring philosophy—and grow into a technical leader within a culture that values innovation, inclusion, and flexibility.

🎯 Requirements

• 2+ years of industry experience applying machine learning methods such as user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, or graph representation learning
• End-to-end hands-on experience building data processing pipelines, large-scale machine learning systems, and working with big data technologies like Hadoop and Spark
• M.S. or PhD in Machine Learning or a closely related technical field
• Expertise in scalable real-time systems that process streaming data
• Passion for applied machine learning and a genuine interest in the Pinterest product and its mission

🏖️ Benefits

• Competitive base salary range of $170,840 to $220,840 CAD, with final compensation based on location, experience, and skills
• Access to Pinterest’s PinFlex working model, which supports flexibility in how and where work is done, guided by the nature of the work and collaboration needs
• Opportunity to work with one of the industry’s most unique and extensive datasets—over 300 billion ideas saved—to build impactful, large-scale recommendation systems
• In-office collaboration 1–2 times per quarter, requiring commutable distance from the Toronto office (85 Richmond St. W), balancing connection with flexibility
• Commitment to an equitable, inclusive, and inspiring workplace, with Pinterest being an equal opportunity employer that values merit-based hiring and considers qualified applicants regardless of background, including criminal history where legally permissible

Skills & Technologies

Apache Spark

Remote

Degree Required

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Pinterest, Inc.

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About Pinterest, Inc.

Pinterest operates a visual discovery platform where users save and share images linked to recipes, home ideas, style inspiration, and other interests. Founded in 2010 and headquartered in San Francisco, the company provides free accounts, visual search tools, and shoppable pins that connect consumers with retailers. Revenue comes primarily from native advertising that appears within users’ feeds and search results. The service is accessible via web and mobile apps worldwide, emphasizing personalized recommendations driven by machine-learning models trained on user activity and image data.

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