Machine Learning Engineer

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At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .

Growth Content Science builds the ML that decides which content we bring to people on our growth surfaces, with a focus on notifications. We own the real-time production stack behind that decision: candidate retrieval, L1 and L2 ranking, utility and blending, content quality along with copy generation and ranking. As a Staff Machine Learning Engineer, you'll personally build the most complex models and systems on this stack, hold the bar for code quality and healthy ML infrastructure and lead the technical direction for notification content ranking.

• Lead multiple technical sub-areas within Growth content science effectively.
• Drive Growth ML model improvements through AB test driven innovation loop - from ideation to production launches.
• Own the health of our ML ecosystem: training pipelines, feature and serving infrastructure, evaluation frameworks and monitoring, AB test set up, etc.
• Help raise the technical excellence bar through mentorship, reviews, evaluations, documentation, standard setting, etc.
• Collaborate and build strong working relationships with colleagues and stakeholders to tackle big, cross-functional, initiatives.
• Work closely with other engineering leaders to form technical directions and roadmaps.
• Operate autonomously, take ownership in complex and evolving environments and drive work effectively.
• Identify and apply relevant state-of-the-art techniques from recent research and/or collaborate on novel research directions.
• Continuously optimize various parts of work by building and using cutting-edge AI tools.

• Graduate degree in Computer Science or in a related field
• Minimum of 8 years of experience in machine learning engineering, including hands-on experience building and shipping models for a large-scale, real-time, end-user production recommender system
• Expertise in recommender modeling, including sequence modeling of user behavior with transformer architectures, DNN rankers and generative and LLM-native recommenders
• Experience in building and operating high-QPS, latency-constrained ML systems, including training and serving infrastructure, and in owning the cost, latency and reliability tradeoffs
• Experience in industry-grade software engineering that others use as the example, and in conducting thorough code and design reviews that raise the quality of a team's work
• Understanding of content personalization and content quality as one problem, and of why cold-start and low-signal users break standard rankers
• Experience in leading technical work across teams, making timely decisions on hard tradeoffs and saying no when the user experience or the…