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Builds state-of-the-art applied ML models for Pinterest's ads ranking (lightweight ranker, engagement, and conversion modeling), designing features and large-scale low-latency models to predict user ad actions. Core stack: PyTorch, Python, C++, and AWS, with NLP and LLM techniques for user-intent and content understanding.
Senior machine learning engineer who builds deep learning models to personalize Pinterest's product surfaces (Homefeed, Ads, Growth, Shopping, Search), improving candidate retrieval and ranking while leveraging LLMs and embeddings for content understanding. Core stack includes PyTorch, TensorFlow, Python, Spark, and SQL.
Senior Machine Learning Engineer at Pinterest building and improving deep learning/ML models that personalize product surfaces (Homefeed, Ads, Growth, Shopping, Search), with a focus on candidate retrieval and large-scale recommendation systems. Core stack includes Java, Python, NLP, and distributed/GPU environments; remote work within the US is permitted.
Machine Learning Engineer II builds and improves deep-learning and recommender models that personalize Pinterest across product surfaces (Homefeed, Ads, Growth, Shopping, Search), improving candidate retrieval and leveraging LLMs for content understanding. Core tech: deep learning, recommender/search ranking, data pipelines, Hadoop, Spark.
Backend software engineer at Pinterest building Pinner-facing features end to end — designing, developing, and operating large-scale distributed systems alongside design, product, and engineering teams. Core stack includes Python, Java, C++, MySQL, with heavy emphasis on automated testing, A/B testing, and data-driven decisions.
Senior ML engineer at Pinterest builds deep-learning models that personalize the app across Homefeed, Ads, Growth, Shopping, and Search, improving candidate retrieval through rapid experimentation. Core stack: Python, SQL, PyTorch/TensorFlow, Spark/Hadoop, and AWS, working hybrid from the Seattle office.
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