Description
We're looking for a highly technical Engineering Manager with a deep understanding of modern recommendation systems to manage, lead, and develop a team of machine learning researchers and engineers within the Applied Science team.
Vision and Strategy: Own the technical roadmap and strategic vision for Pinterest's next-generation recommendation systems. Champion the use of state-of-the-art ML techniques to deliver revolutionary innovations in recommendation technology.
Research to Production: Successfully transition breakthrough ML research into production-ready systems that directly impact core company metrics.
Team Leadership and Culture: Manage, inspire, and develop a talented team of machine learning researchers and engineers specializing in recommendation systems. Partner with your team to define their charter, ensuring a strong balance between cutting-edge research and building foundational embeddings that benefit products across the entire company.
Cross-functional Collaboration: Collaborate with Core Engineering, Ads Engineering, Infrastructure, Content, and Data Science teams to prototype, build, and scale solutions to complex engineering challenges. Partner with leadership to deepen user understanding and set the strategic direction for our recommendation system roadmap.
Requirements:
- Technical Depth: 7+ years of combined post-graduate academic and industry experience applying state-of-the-art ML technologies to real-world problems on web-scale data, alongside 3+ years of direct people management experience.
- Proven Execution: A track record of delivering high-impact initiatives across multiple product areas, with a demonstrated ability to influence peers and leadership using data-driven insights.
- Talent Development: Experience mentoring, coaching, and up-leveling software and machine learning engineers.
- Continuous Learner: A self-propelled learner who stays ahead of industry trends, new tools, and emerging methodologies, with an appetite for building proof-of-concept prototypes.
- Business & Product Acumen: The ability to transform vague, ambiguous questions into well-defined projects with clear success metrics that drive business decisions.
- Communication & Credibility: Excellent communication skills with the ability to distill complex technical findings for leadership and product teams, backed by a strong track record of publications in machine learning, AI, data science, or related technical fields.
- Academic Credentials: MS/PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related field
Nice to have:
- Track record of publishing at top-tier ML/RecSys conferences (KDD, RecSys, NeurIPS, CVPR).
- Experience leveraging modern LLM/Agentic workflows and generative AI capabilities to accelerate engineering productivity and context extraction.