Master's University Grad Machine Learning Engineer 2027 (USA)
About this role
Pinterest builds visual discovery platforms and personalized recommendation systems that serve hundreds of millions of global users. Machine learning engineers on the team contribute to large-scale recommendation systems, search infrastructure, ads ranking, natural language processing, and graph representation learning. This hiring initiative pools talent across various machine learning teams to tackle massive scale and exponential growth challenges.
New graduates step into a high-impact environment with hands-on access to vast data vaults. Engineers in this track design engagement-prediction models, build content recommendation frameworks for notifications, and apply graph analysis to diverse discovery problems. Ideal candidates hold a relevant Master's degree, demonstrate solid foundational machine learning skills, and bring a collaborative approach to working alongside modern AI-native engineering tools.
What you will do
- Contribute to machine learning and artificial intelligence research applied directly to platform problems.
- Collect, analyze, and synthesize data findings to build intelligent, data-driven models.
- Write clean, efficient, and sustainable code for production environments.
- Apply machine learning, natural language processing, and graph analysis to solve ranking and modeling challenges across discovery, ads, and search.
- Design, build, and test models to predict engagement for push, email, and in-app notifications.
- Build content recommendation systems that power core notification channels.
- Work on large-scale applied machine learning projects and independently scope solutions for moderately complex problems.
What you need
- Must hold a Master's degree in Computer Science, Machine Learning, Natural Language Processing, Statistics, Information Sciences, or a related field.
- Demonstrate prior machine learning experience covering areas such as ranking, computer vision, natural language processing, content recommendations, embeddings, or information retrieval.
- Bring experience with big data technologies like Hadoop or Spark and scalable real-time stream processing systems.
- Show mastery of at least one systems language such as Java, C++, or Python, or proficiency with machine learning frameworks like TensorFlow, PyTorch, or MLFlow.
- Demonstrate proficiency with AI-native engineering, including designing agent-friendly codebases and utilizing agent-first development tools.
- Apply strong critical thinking to validate the correctness, performance, security, and maintainability of AI-generated suggestions.
- Communicate effectively and collaborate smoothly as a team player when solving open-ended problems.
Benefits
- Equity eligibility
Skills and tech stack
Questions candidates ask
What is the remote or in-office policy for this role?
The type of work you do guides the collaboration style, meaning teams are not always working in an office but gather for key moments of collaboration and connection. This role may require you to be located near an office for in-person collaboration, and therefore may need to be located a commutable distance from one of the Pinterest offices.
What is the salary range for this position?
The base salary range for this position is $146,000 to $196,000 USD. The position is also eligible for equity, with final compensation based on factors including location, travel, relevant prior experience, or particular skills and expertise.
What kind of systems and tools will I work with?
Engineers work with big data technologies such as Hadoop and Spark, scalable real-time systems that process stream data, and systems languages or machine learning frameworks including Java, C++, Python, TensorFlow, PyTorch, and MLFlow.
What teams or problem spaces will I be considered for?
By applying to this role, you will be considered for multiple roles open across various machine learning teams. You will have the opportunity to work on a very large scope of problems in recommender systems, search, ads, ranking, natural language processing, graph representation learning, and personalization.