Pinterest helps people Discover and Do the things they love. We have more than 500M monthly active users who actively curate an ecosystem of more than 400B Pins on more than 8B boards, creating a rich human curated graph of immense value. 

Pinterest builds an internet scale personalized recommendation engine in 30+ languages, which requires a deep understanding of the users and content on our platform. As a staff machine learning engineer for the content understanding team, you will be responsible for developing horizontal knowledge graph and content understanding signals, from modeling to serving, and adopting them for various recommendation systems in Pinterest. 

What you’ll do:

• Utilize state of the art machine learning, natural language processing, and multimodal modeling techniques to build content signals that power personalized product experience across Pinterest ecosystems (discovery, growth, ads etc).
• Gather, examine, and integrate findings from data to build effective data-driven models.
• Partner with surface engineering teams and product team to discover opportunities to improve recommendation on Pinterest through content/user understanding.
• Drive team level tech strategy, and solve complex problems independently.

What we’re looking for:

• MS/PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related fields.
• 5+ years of industry experience in machine learning in industry and 1+ years of TL in use cases with large scale: content understanding, recommendation systems, information retrieval.
• Experience with Generative AI and LLM.
• Hands-on experience working with large scale ML modeling development and productization.  
• Effective collaborator working with cross functional partners and an excellent communicator.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement:

• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration 1 time per week and therefore needs to be in a commutable distance from our offices.

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