PhD University Grad Machine Learning Engineer (USA)
About this role
Engineers in this group research, design, and build large-scale machine learning models that power recommendations, search, ads, and notifications for hundreds of millions of users. Daily responsibilities involve gathering and analyzing large datasets, writing sustainable code, and developing intelligent systems that predict user engagement across various platform surfaces.
Pinterest maintains a massive data repository, giving the team hands-on access to solve complex problems in natural language processing, graph representation learning, ranking, and personalization. Individuals joining the engineering organization will contribute to applied ML projects that shape how users discover visual ideas and inspiration.
The position exists to scale the platform's personalization and discovery capabilities through advanced algorithmic research and implementation. Strong candidates hold a relevant doctorate, possess solid software engineering fundamentals, and demonstrate an ability to solve open-ended technical problems independently.
What you will do
- Contribute to machine learning and artificial intelligence research to solve platform-specific 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 resolve modeling and ranking challenges across discovery, ads, and search.
- Design, build, and test predictive models for notifications including push, emails, and in-app alerts.
- Build content recommendation systems to power user notification channels.
- Work on state-of-the-art large-scale applied machine learning projects and independently solve moderately complex problems.
What you need
- Hold a PhD in Computer Science, Machine Learning, Natural Language Processing, Statistics, Information Sciences, or a related field.
- Possess practical machine learning experience in areas such as ranking, computer vision, content recommendations, embeddings, or information retrieval.
- Demonstrate experience with big data technologies like Hadoop or Spark alongside scalable real-time stream processing systems.
- Show mastery of at least one systems language including Java, C++, or Python, or a primary machine learning framework such as TensorFlow, PyTorch, or MLFlow.
- Display proficiency in AI-native engineering principles, including the design of agent-friendly codebases and agent-first development tools.
- Exhibit strong critical thinking when evaluating AI-generated suggestions for correctness, performance, security, and maintainability.
- Demonstrate strong communication skills, teamwork, and the ability to find solutions for open-ended problems.
Nice to have
- Publications in machine learning, AI, data science, data analytics, statistics, or related technical fields
- Interest in research and in applying ML to impactful real-world problems on the Pinterest product
Benefits
- Equity eligibility
- Flexibility to do your best work with hybrid collaboration
Skills and tech stack
Questions candidates ask
What is the remote and office work policy?
The company lets the type of work you do guide the collaboration style. This role may require you to be located near an office for in-person collaboration and therefore needs to be a commutable distance from a Pinterest office.
What is the salary range for this position?
The base salary range for this position is $174,078 to $229,044 USD, and the position is also eligible for equity.
What technologies and frameworks will I use?
Engineers work with big data technologies like Hadoop and Spark, systems languages such as Java, C++, and Python, and ML frameworks including TensorFlow, PyTorch, and MLFlow.
What does the hiring and interview process look like?
The interview process explores foundational skills and how candidates collaborate with AI. What matters most is that applicants can always explain their approach, showing not just what they know, but how they think.