University Grad Machine Learning Engineer – Toronto

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

At Pinterest, AI isn’t just a feature, it’s a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

As a Machine Learning Engineer at Pinterest, you’ll develop and improve machine learning systems that power product experiences and discovery. You’ll partner with engineering, product and design teams to build, evaluate and iterate on scalable solutions that help Pinners find relevant, inspiring content. In this role, you’ll grow your technical expertise while contributing to reliable, high‑impact systems.

What you’ll do:

• Conduct applied machine learning research to address product and platform challenges at Pinterest.
• Collect, analyze and synthesize data to develop data‑driven machine learning models.
• Write maintainable, efficient code and contribute to testing and code‑review practices.
• Apply machine learning techniques, including natural language processing and graph‑based approaches, to modeling and ranking challenges across discovery, ads and search.
• Design, build and test recommendation and engagement‑prediction models that improve push, email and in‑app notification experiences.
• Develop and iterate on large‑scale applied machine learning systems, using evaluation results to improve model performance and reliability.
• Scope, implement and deliver solutions for moderately complex ML problems, escalating risks and tradeoffs as needed.
• Experience applying machine learning or AI concepts through research, coursework, projects or internships.
• Use AI to accelerate analysis and iteration while applying judgment and verification to ensure correctness and quality.

What we’re looking for:

• Master’s in Computer Science, ML, NLP, Statistics, Information Sciences or related field required.
• Machine Learning experience (ranking, computer vision, NLP, content recommendations, embedding, information retrieval etc).
• Experience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data.
• Mastery of at least one systems languages (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow).
• Cross‑functional collaborator and strong communicator.
• Comfortable solving ambiguous problems and adapting to a dynamic environment.
• Experience in research and in solving analytical problems.

In‑Office Requirement:

This onsite internship will be based in our Toronto office requiring a few days in office. Our program will run in a full‑time capacity during typical business hours between Monday‑Friday. This role will need to be in the office for in‑person collaboration 2‑3 weekdays per week and therefore needs to be within a commutable distance of Toronto.

Relocation Statement:

This position may be eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

Salary Range:

$135,000-$162,000 CAD

Our Commitment to Inclusion:

Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws.