Senior Manager, Machine Learning Engineering-Applied Research

We're looking for a highly technical Senior Engineering Manager with a deep understanding of modern recommendation systems to manage, lead and develop a team of ML researchers and engineers within the Applied Science team.  In this role, you will lead the strategy of the Recommendation System innovation team.  You will help the team build a balanced portfolio of work that addresses both immediate short-term business impact and long-term strategic breakthroughs in areas like Generative Recommendation System (GenRecSys), Large Sequential Models, and Reinforcement Learning (RL).  Partnering closely with executive leadership, product management, and engineering partners, you will evolve our technical roadmap and directly drive Pinterest’s core mission forward. 

What you will do:

• Technical Vision and Execution: 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.
• Team Leadership & People Management: Support, mentor, and grow a world-class team of Applied Scientists, Machine Learning Engineers, and technical leads. Recruit top talent, foster an inclusive research-to-production culture, and guide career progression.
• Portfolio & Strategy Management: Balance execution across two parallel horizons—delivering near-term measurable metric improvements while sponsoring multi-quarter, high-risk/high-reward AI/ML research initiatives.
• Research to Production: Successfully transition breakthrough ML research into production-ready systems that directly impact core company metrics.
• Cross-functional Partnership: Collaborate seamlessly with Consumer Engineering, Ads Engineering, Platform, 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.

What we are looking for:

• 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: B.S. in Computer Science, ML, NLP, Statistics, Information Sciences or related field

Preferred Qualifications

• Experience managing hybrid teams of research-oriented scientists and production-focused machine learning engineers.
• Track record of publishing at top-tier ML/RecSys conferences (KDD, RecSys, NeurIPS, CVPR).
• Familiarity with generative AI for recommendations (e.g., semantic indexing, LLMs/GenRec architectures) or deep RL for long-term user value optimization.
• Experience leveraging modern LLM/Agentic workflows and generative A

In-Office Requirement Statement:

• 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.
• This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

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