Staff Machine Learning Engineer

Pinterest•San Francisco, CA

1d•$189,308 - $389,753•Remote

About The Position

Growth Content Science builds machine learning systems that decide which content appears on Pinterest’s growth surfaces, particularly notifications. The team owns the real-time production stack for these decisions, including candidate retrieval, first- and second-level ranking, utility and blending, content quality, and copy generation and ranking. The Staff Machine Learning Engineer will build complex models and systems, uphold high standards for code quality and healthy ML infrastructure, and lead the technical direction for notification content ranking. The role leads multiple technical areas, advances Growth ML through an A/B-test-driven cycle from idea to production launch, and owns the health of the ML ecosystem, including training pipelines, feature and serving infrastructure, evaluation and monitoring, and A/B test setup. It also involves setting technical standards, mentoring and reviewing work, collaborating across functions, shaping technical direction and roadmaps with engineering leaders, and applying relevant research and AI tools. The role requires autonomous ownership in complex, evolving environments and in-person collaboration at the office 1–2 times per quarter; it can be based anywhere in the country.

Requirements

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 sequence modeling of user behavior with transformer architectures, DNN rankers, and generative and LLM-native recommenders.
Experience building and operating high-QPS, latency-constrained ML systems, including training and serving infrastructure, and owning cost, latency, and reliability tradeoffs.
Industry-grade software engineering skills and experience conducting thorough code and design reviews that improve team quality.
Understanding of content personalization and content quality as connected problems, and of how cold-start and low-signal users challenge standard rankers.
Experience leading technical work across teams, making timely decisions on difficult tradeoffs, and declining work when user experience or system health is at stake.
Experience mentoring junior engineers.
Outstanding written and verbal communication skills, particularly in cross-functional settings.
Demonstrated experience using AI to improve speed and quality in applied research.
Strong track record of publications and patents.

Responsibilities

Lead multiple technical sub-areas within Growth Content Science.
Drive Growth ML model improvements through an A/B-test-driven innovation cycle, from ideation to production launches.
Own the health of the ML ecosystem, including training pipelines, feature and serving infrastructure, evaluation frameworks and monitoring, and A/B test setup.
Raise technical excellence through mentorship, reviews, evaluations, documentation, and standard setting.
Collaborate with colleagues and stakeholders on large cross-functional initiatives.
Work with engineering leaders to shape technical direction 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 research and/or collaborate on novel research directions.
Optimize work by building and using cutting-edge AI tools.