Machine Learning Engineer (Master's University Grad, USA, 2027)
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Description
- As a Machine Learning Engineer at Pinterest, you’ll work on tackling new challenges in machine learning and artificial intelligence
- As you kickstart your career at Pinterest, you’ll join our engineering teams as we maneuver through exponential growth and massive scale while building awesome products and features, creating visually rich experiences, spearheading the discovery problem, and pinpointing tomorrow’s engineering challenges
- Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems
- Collect, analyze, and synthesize findings from data and build intelligent data-driven model
- Write clean, efficient, and sustainable code
- Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across discovery, ads and search
- Design, build, and test models to predict engagement for notifications (push, emails, in-app notifications)
- Build content recommendation systems to power our push, email, and in-app notifications
- Work on state-of-the-art large-scale applied machine learning projects
- Scope and independently solve moderately complex problems
- 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
- Experience in research and in solving analytical problems
- Proficiency with AI-native engineering, including the design of agent-friendly codebases
- High degree of autonomy in learning new agent-first development tools
- Strong critical thinking when working with AI-generated suggestions, with a clear approach to validating correctness, performance, security, and maintainability
- Mastery of at least one systems languages (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)
- Strong communicator and team player. Being able to find solutions for open-ended problems
- Comfort iterating on prompts, refining workflows, and adapting AI-assisted approaches based on the problem, context, and constraints