Sr. Staff Machine Learning Engineer - Applied Research Science
At Pinterest Labs, you'll join a world-class team of research scientists and machine learning engineers to tackle cutting-edge challenges in machine learning and artificial intelligence. This role places you at the intersection of applied research and scalable infrastructure, focusing heavily on ML framework and efficiency.
You will conduct research that can be applied across Pinterest engineering teams, engaging in external collaborations and mentoring. Your research focus will specifically target ML efficiency and large-scale infrastructure challenges within high-impact areas such as: generative recommender systems, post-training, reinforcement learning, multi-modality representation learning, and graph neural networks.
What you’ll do:
Design, develop, maintain, and enhance advanced machine learning solutions across various key business areas.
Lead the technical strategy for optimizing and improving the efficiency of large-scale ML infrastructure.
Lead high-impact machine learning projects, overseeing priorities, deadlines, and deliverables while providing technical guidance.
Drive alignment and clarity on goals, outcomes, and timelines across teams.
What we’re looking for:
MS/PhD in Computer Science or a related field degree.
10+ years of industry experience.
Experience in distributed system, ML frameworks (e.g. Pytorch), scaling laws.
Experience in research and in solving analytical problems.
Cross-functional collaborator and strong communicator.
Comfortable solving ambiguous problems and adapting to a dynamic environment.
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 quarter and therefore can be situated anywhere in the country.
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
#LI-REMOTE #LI-AK7
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Sr. Staff Machine Learning Engineer - Applied Research Science
At Pinterest Labs, you'll join a world-class team of research scientists and machine learning engineers to tackle cutting-edge challenges in machine learning and artificial intelligence. This role places you at the intersection of applied research and scalable infrastructure, focusing heavily on ML framework and efficiency.
You will conduct research that can be applied across Pinterest engineering teams, engaging in external collaborations and mentoring. Your research focus will specifically target ML efficiency and large-scale infrastructure challenges within high-impact areas such as: generative recommender systems, post-training, reinforcement learning, multi-modality representation learning, and graph neural networks.
What you’ll do:
Design, develop, maintain, and enhance advanced machine learning solutions across various key business areas.
Lead the technical strategy for optimizing and improving the efficiency of large-scale ML infrastructure.
Lead high-impact machine learning projects, overseeing priorities, deadlines, and deliverables while providing technical guidance.
Drive alignment and clarity on goals, outcomes, and timelines across teams.
What we’re looking for:
MS/PhD in Computer Science or a related field degree.
10+ years of industry experience.
Experience in distributed system, ML frameworks (e.g. Pytorch), scaling laws.
Experience in research and in solving analytical problems.
Cross-functional collaborator and strong communicator.
Comfortable solving ambiguous problems and adapting to a dynamic environment.
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 quarter and therefore can be situated anywhere in the country.
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
#LI-REMOTE #LI-AK7