Principal Engineer, Shopping & Delivery Infra

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

Pinterest is looking for a Principal Engineer to lead the technical strategy and execution for our Indexing & Retrieval Infrastructure across Core, Ads, and Shopping. This role sits at the center of our delivery infrastructure organization and will shape how fresh, relevant, and cost-efficient candidates are generated for the discovery experiences that power Pinterest at scale.

• →Define and drive the long-term technical vision for indexing and retrieval infrastructure across Core, Ads, and Shopping, aligning architecture investments to measurable improvements in freshness, quality, coverage, reliability, and cost efficiency.
• →Lead cross-org initiatives to modernize Pinterest’s indexing architecture, including advancing unified real-time and incremental retrieval systems that support high-scale, high-quality candidate generation.
• →Partner closely with engineering, product, ML, and infrastructure leaders to align priorities, resolve tradeoffs, and deliver shared platform capabilities that improve retrieval performance across multiple discovery surfaces.
• →Raise the technical bar for retrieval infrastructure by guiding system design, architecture reviews, and execution on complex platform changes that span document ingestion, indexing, retrieval, and quality evaluation.
• →Build shared platform capabilities such as common metrics, tuning surfaces, and debugging workflows that help teams understand and improve the tradeoffs between relevance, safety, engagement, revenue, and infrastructure cost.
• →Mentor senior engineers and technical leaders across teams, fostering strong engineering judgment, inclusive collaboration, and high standards for operational excellence.
• →Use AI to accelerate analysis, prototyping, and iteration on system design options, while applying engineering judgment and verification to ensure correctness, reliability, and quality.
• →Use AI to streamline repeatable work such as summarizing technical investigations, drafting design artifacts, and synthesizing platform insights so teams can move faster on high-impact decisions.

• Deep expertise building and evolving large-scale indexing, search, recommendation, or ads retrieval systems, including real-time and incremental architectures, relevance tradeoffs, and cost-efficient distributed infrastructure.
• Strong technical leadership experience operating across multiple teams or organizations, with a track record of influencing direction, aligning stakeholders, and driving complex platform decisions to completion.
• Proven ability to lead large modernization efforts such as architectural convergence, migration from legacy systems, rollout of shared platforms, or introduction of new infrastructure capabilities at scale.
• Exceptional systems thinking and problem-solving skills, with the ability to connect platform design decisions to downstream product quality, ML needs, operational health, and business outcomes.
• Strong communication skills, including the ability to distill complex technical topics for senior technical and non-technical audiences, and to drive clarity in ambiguous, high-impact spaces.
• Demonstrated experience using AI to improve speed and quality in day-to-day engineering workflows, such as technical analysis, prototyping, design exploration, documentation, or synthesis of complex information.
• Strong track record of critically evaluating and validating AI-assisted work through testing, source-checking, peer review, and sound engineering judgment, with clear accountability for final decisions and deliverables.
• High integrity, ownership, and care in handling sensitive data and production-critical systems, including thoughtful use of AI without over-reliance and with strong attention to privacy, safety, and correctness.
• Bachelor’s or Master’s degree in Computer Science, Engineering, a related technical field, or equivalent experience.