Machine Learning Engineer

Pinterest builds a platform for discovering creative ideas and planning. Growth Content Science develops machine learning for content selection on growth surfaces, especially notifications, including candidate retrieval, ranking, utility and blending, content quality, and copy generation and ranking.

• Build complex models and systems for notification content ranking and lead its technical direction.
• Lead multiple technical sub-areas within Growth Content Science.
• Drive machine learning model improvements through an A/B-test-driven process, from ideation to production launch.
• Own the health of the machine learning ecosystem, including training pipelines, feature and serving infrastructure, evaluation frameworks, monitoring, and A/B test setup.
• Raise technical excellence through mentorship, reviews, evaluations, documentation, and setting standards.
• Collaborate with colleagues and stakeholders on cross-functional initiatives and work with engineering leaders on technical direction and roadmaps.
• Work autonomously, take ownership in complex and evolving environments, and drive work effectively.
• Apply state-of-the-art techniques from recent research and/or collaborate on novel research directions.
• Optimize work by building and using cutting-edge AI tools.

• Graduate degree in Computer Science or a related field.
• At least 8 years of machine learning engineering experience, including hands-on experience building and shipping models for a large-scale, real-time, end-user production recommender system.
• Expertise in recommender modeling, including user-behavior sequence modeling with transformer architectures, DNN rankers, and generative and LLM-native recommenders.
• Experience building and operating high-QPS, latency-constrained machine learning systems, including training and serving infrastructure, and managing cost, latency, and reliability tradeoffs.
• Industry-grade software engineering experience and thorough code and design reviews that raise team quality.
• Understanding of content personalization and content quality as related problems, including why cold-start and low-signal users challenge standard rankers.
• Experience leading technical work across teams, making timely decisions on difficult tradeoffs, and prioritizing user experience and system health.
• Experience mentoring junior engineers.
• Excellent written and verbal communication, especially in cross-functional settings.
• Demonstrated experience using AI to improve speed and quality in applied research.
• Strong record of publications and patents.

• Remote work in the US, with in-person office collaboration required 1–2 times per quarter; the role can be based anywhere in the country.
• Not eligible for relocation assistance.
• US-based applicants only. Base salary range is $189,308–$389,753 USD per year; the position is also eligible for equity.