Staff Machine Learning Engineer - Applied Science (Recommendation Systems)
To see similar active jobs please follow this link: Remote Development jobs
Pinterest Labs is a group dedicated to using machine learning to bring inspiration to everyone. Tackling some of the most challenging problems in ML and AI, Labs solves the problem of discovery online and doing it in an inspiring and inclusive way.
In this role, you'll work on tackling new challenges in machine learning and artificial intelligence along with a world-class team of research scientists, and machine learning engineers. You'll conduct research that can be applied across Pinterest engineering teams and engage in external collaborations and mentoring, while also performing research in any of the following areas: computer vision, graph neural network, natural language processing (NLP), inclusive AI, reinforcement learning, user modeling, and recommender systems.
What you’ll do:
Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems
Collect, analyze, and synthesize findings from data and build intelligent data-driven model
Write clean, efficient, and sustainable code
Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across growth, discovery, ads and search
Scope and independently solve moderately complex problems
What we’re looking for:
MS/PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related field
6+ years of industry experience
Experience in machine learning/information retrieval/ recommendation systems
Mastery of at least one systems languages (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)
Experience in research and in solving analytical problems
Cross-functional collaborator and strong communicator
Comfortable solving ambiguous problems and adapting to a dynamic environment
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
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About the job
Staff Machine Learning Engineer - Applied Science (Recommendation Systems)
To see similar active jobs please follow this link: Remote Development jobs
Pinterest Labs is a group dedicated to using machine learning to bring inspiration to everyone. Tackling some of the most challenging problems in ML and AI, Labs solves the problem of discovery online and doing it in an inspiring and inclusive way.
In this role, you'll work on tackling new challenges in machine learning and artificial intelligence along with a world-class team of research scientists, and machine learning engineers. You'll conduct research that can be applied across Pinterest engineering teams and engage in external collaborations and mentoring, while also performing research in any of the following areas: computer vision, graph neural network, natural language processing (NLP), inclusive AI, reinforcement learning, user modeling, and recommender systems.
What you’ll do:
Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems
Collect, analyze, and synthesize findings from data and build intelligent data-driven model
Write clean, efficient, and sustainable code
Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across growth, discovery, ads and search
Scope and independently solve moderately complex problems
What we’re looking for:
MS/PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related field
6+ years of industry experience
Experience in machine learning/information retrieval/ recommendation systems
Mastery of at least one systems languages (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)
Experience in research and in solving analytical problems
Cross-functional collaborator and strong communicator
Comfortable solving ambiguous problems and adapting to a dynamic environment
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
#LI-HYBRID
#LI-SA1