In ~1 minute you get: who interviews you, the likely questions answered from your CV, and your CV tailored to this job. Free, no card.

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. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. 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. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . 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 This job posting is for an open vacancy. Please note that the company utilizes artificial intelligence to screen applicants for the positions. In-Office Requirement: • This onsite internship will be based in our Tor

University Grad Machine Learning Engineer 2027 (Toronto)

Don't apply unprepared

University Grad Machine Learning Engineer 2027 (Toronto)

Don't apply unprepared