We are looking for a Sr. Staff Machine Learning Engineer to be the Technical Lead for the Content Quality who will build the overall technical strategy, unified technical architecture and define a roadmap for industry leading methodology. We are seeking strong hands on machine learning background including content modeling, signal lifecycle, and platforms used to enforce signal use with downstream use cases.
You’ll be working with other leads to set and execute a long-term strategy for the team, aligning the strategy with other clients where it makes sense and communicating to leadership our current status and path to having world-class capabilities. You'll also foster a healthy community where all Content Quality engineers can learn best practices, collaborate effectively and understand our technical direction.
- Architect and develop a roadmap and processes for building and delivering signals capturing quality and trust aspects of content at Pinterest.
- Drive safety of GenAI and Conversational use cases including safety alignment and VLMs.
- Work with downstream teams to align on use cases, evaluate signal impact, and drive adoption of signals in models, ranking systems, and decision-making workflows.
- Partner closely with ML engineers to translate ideas into production-ready signals, from problem formulation and feature design to validation and deployment.
- Experience driving technical strategy at an organizational level.
- Strong ability to work cross-functionally and with partner engineering teams.
- Bachelor’s/Master’s degree in a relevant field such as computer science, or equivalent experience.
- Hands-on experience with big data technologies (e.g., Hadoop / Spark / Kafka / Flink) is a plus.
- Machine Learning at scale deployment experience (note this is different from having ML theoretical knowledge, which is a nice to have).
- Thought Leadership: Publication and/or conference speaking experience is a plus.
- Bachelor’s degree in a relevant field such as Computer Science or equivalent experience
- Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.
- Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.
- 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.