Role Description & Responsibilities
Machine Learning
About Pinterest
Pinterest is a high-growth technology leader building scalable solutions for global users. Millions of companies and teams rely on their infrastructure and services to execute high-value workflows, analyze massive datasets, and scale modern operations.
CadetX Candidate Advantage: As an applicant for this Machine Learning role, you are evaluated on practical proficiency with real-world query execution, data pipelines, and clear stakeholder communication.
CadetX Role Brief & Company Specifications
Role Mission & Impact
As a Machine Learning Intern at Pinterest, you’ll apply machine learning and AI to product and engineering challenges. You’ll work with engineering, product and data teams to prototype, evaluate and improve ML-powered features and discovery experiences. Throughout your internship, you’ll contribute to real projects, learn how large-scale ML systems are developed and build practical skills with guidance from experienced engineers.
This job posting is for an open vacancy. Please note that the company utilizes artificial intelligence to screen applicants for the positions.
Key Responsibilities & Day-to-Day Scope
- ✓ Own a scoped machine learning project from problem definition through experimentation, implementation and communication of results, with guidance from your manager and team
- ✓ Contribute to applied machine learning and AI research that informs Pinterest product experiences
- ✓ Collect, analyze and synthesize data to develop data-driven machine learning models
- ✓ Write clean, efficient and maintainable code, supported by appropriate documentation and testing
- ✓ Apply relevant machine learning techniques, including natural language processing or graph-based approaches, to modeling and ranking problems across discovery, ads and search
- ✓ Break down scoped technical problems, propose approaches and implement solutions with guidance from the team
- ✓ Deliver assigned project milestones, share progress and findings with teammates and seek guidance when 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.
- ✓ Working towards a Master's or PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field
Official Qualifications & Technical Requirements
- • Machine Learning (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
- • Proficiency in at least one systems language (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)
- • Experience in research and in solving analytical problems
- • Strong communicator and team player with the ability to find solutions for open-ended problems
- • 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 .
- • This position may be eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
- • Information regarding the culture at Pinterest and benefits available for this position can be found here .
Preferred Skills & Technology Stack
- + Familiarity with MLOps pipelines (Docker, Kubernetes, Kubeflow, Triton Inference Server).
- + Knowledge of distributed training techniques and GPU acceleration (CUDA).
- + Relevant coursework, research publication, or open-source contribution in ML/DL.
Tech Stack & Tools in this role
Expected technical competencies for Day 1 productivity:
★ Life & Culture at Pinterest
Verified EmployerPinterest champions rigorous analytical discipline, continuous learning, and low-ego teamwork. Team members enjoy flexible hybrid allowances, learning stipends, and structured mentorship to fast-track career mobility.