Staff Machine Learning Engineer
Pinterest helps Pinners discover and do what they love. At the core of this discovery experience is Pinterest search. The Search Quality team is focused on making Pinterest search fast, personalized, and relevant for the billions of monthly searches. We seek a Tech Lead for ranking who can drive cross-team engineering efforts to deliver ML-driven product experiences to our Pinners. This role offers the opportunity to work on innovative projects, build large-scale low-latency systems and state-of-the-art machine learning models, and significantly impact our Pinners and business metrics.
What you'll do:
Build foundational models to do semantic search relevance.
Work with teams responsible for search full-stack to improve relevance at each stage.
Develop relevance focused signals (e.g. VLM based embeddings) that can be used in multiple models across the search stack.
Contribute towards the strategic vision and technical direction of highly visible initiatives.
Mentor a team of 5 engineers.
Work cross functionally with adjacent teams to drive cohesive modeling across the business.
What we're looking for:
Languages: Python, Java.
Machine Learning: PyTorch, TensorFlow.
Big data processing: Spark, Hive, MapReduce.
Experience with search and in particular semantic relevance modeling (Preferred).
Hands on experience leveraging LLMs and VLMs to drive product impact.
Strong modeling skills and experience working on problems like online/offline bias, scaling models, etc.
Experience with large scale systems.
Exposure to Generative AI models.
Bachelors degree in a relevant field such as computer science, or equivalent experience
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 1-2 times/quarter and therefore can be situated anywhere in the country.
#LI-REMOTE
#LI-DM57
About the job
Apply for this position
Staff Machine Learning Engineer
Pinterest helps Pinners discover and do what they love. At the core of this discovery experience is Pinterest search. The Search Quality team is focused on making Pinterest search fast, personalized, and relevant for the billions of monthly searches. We seek a Tech Lead for ranking who can drive cross-team engineering efforts to deliver ML-driven product experiences to our Pinners. This role offers the opportunity to work on innovative projects, build large-scale low-latency systems and state-of-the-art machine learning models, and significantly impact our Pinners and business metrics.
What you'll do:
Build foundational models to do semantic search relevance.
Work with teams responsible for search full-stack to improve relevance at each stage.
Develop relevance focused signals (e.g. VLM based embeddings) that can be used in multiple models across the search stack.
Contribute towards the strategic vision and technical direction of highly visible initiatives.
Mentor a team of 5 engineers.
Work cross functionally with adjacent teams to drive cohesive modeling across the business.
What we're looking for:
Languages: Python, Java.
Machine Learning: PyTorch, TensorFlow.
Big data processing: Spark, Hive, MapReduce.
Experience with search and in particular semantic relevance modeling (Preferred).
Hands on experience leveraging LLMs and VLMs to drive product impact.
Strong modeling skills and experience working on problems like online/offline bias, scaling models, etc.
Experience with large scale systems.
Exposure to Generative AI models.
Bachelors degree in a relevant field such as computer science, or equivalent experience
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 1-2 times/quarter and therefore can be situated anywhere in the country.
#LI-REMOTE
#LI-DM57