Pinterest Interview

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Pinterest Interview Process: Complete 2026 Guide

Interviewed at Pinterest in summer 2023 for a backend engineer position. Got to final round but didn’t get the offer. Here’s everything I learned about their process.

Overview

Pinterest is at an interesting scale – 400+ million monthly users but not quite FAANG-sized. The interview reflects this: rigorous technical bar similar to mid-tier FAANG, with emphasis on practical engineering and building features users love.

They care deeply about visual search, recommendations, and serving content at scale. If you’re into ML, computer vision, or recommendation systems, Pinterest is fascinating.

Interview Structure

Recruiter Screen (30 minutes):

  • Background and experience
  • Why Pinterest?
  • Timeline and logistics
  • Compensation expectations

Technical Phone Screen (45-60 minutes):

  • 1-2 coding problems
  • CoderPad or similar platform
  • Medium difficulty
  • Some discussion of your past work

My phone screen: Design a rate limiter (implementation + API). Then discussed scaling considerations.

Virtual Onsite (4-5 hours):

  • 2 coding rounds (45 min each)
  • 1 system design round (60 min)
  • 1 behavioral/culture fit round (30 min)
  • 15 min breaks between rounds

Technical Focus Areas

1. Data Structures & Algorithms (Core)

Expect medium to hard leetcode:

  • Trees and graphs (BFS, DFS)
  • Hash tables and sets
  • Heaps and priority queues
  • Two pointers, sliding window
  • Some dynamic programming

Similar difficulty to Meta/Google for equivalent levels.

2. System Design (Very Important)

Pinterest-specific problems come up:

  • Design a feed system (Pins feed)
  • Design image storage and retrieval
  • Design a recommendation engine
  • Design search functionality
  • Design analytics pipeline

Focus on:

  • Handling millions of images
  • Real-time vs batch processing
  • Personalization at scale
  • CDN and caching strategies

3. Backend Engineering

Strong backend skills expected:

  • API design
  • Database design (SQL + NoSQL)
  • Caching strategies
  • Message queues
  • Microservices architecture

4. Python/Java Skills

Pinterest uses Python and Java heavily:

  • Strong Python or Java proficiency
  • Understanding of frameworks (Flask, Django, Spring)
  • Code quality and best practices

Coding Interview Details

Round 1 – Algorithms:

Problem I got: “Given a list of Pins (images) that a user has saved, find groups of similar Pins.”

This was about clustering/grouping. They wanted:

  • Graph representation (Pins as nodes, similarity as edges)
  • Connected components algorithm
  • Time/space complexity analysis
  • Discussion of how to define “similarity”

Very Pinterest-specific flavor.

Round 2 – Implementation:

Problem: “Implement a simplified version of Pinterest’s ‘Related Pins’ feature.”

Required:

  • Data structure design
  • Efficient similarity calculation
  • Caching considerations
  • API design

More about practical engineering than pure algorithms.

System Design Interview

Question: “Design Pinterest’s home feed system that shows personalized Pins to users.”

Key areas to cover:

  1. Data Model:
    • Users, Pins, Boards
    • Followers, interests, engagement
    • Relationships between entities
  2. Feed Generation:
    • Candidate generation (which Pins to consider)
    • Ranking algorithm
    • Personalization based on user interests
    • Diversity and freshness
  3. Scale:
    • 400M+ users
    • Billions of Pins
    • Real-time updates
    • Caching strategies
  4. Infrastructure:
    • CDN for images
    • Database sharding
    • Message queues for async processing
    • A/B testing infrastructure

The interviewer pushed on ML aspects – “How would you incorporate machine learning? How do you handle cold start for new users?”

Behavioral Interview

Pinterest culture emphasizes:

  • User focus: Making Pinners happy
  • Data-driven: A/B test everything
  • Collaboration: Cross-functional teams
  • Impact: Shipping features that move metrics

Questions I got:

  • “Tell me about a time you used data to make a decision.”
  • “Describe a feature you shipped that didn’t work as expected.”
  • “How do you prioritize when there are competing demands?”
  • “What would you build if you joined Pinterest?”

That last question matters – have a thoughtful answer ready.

Preparation Strategy

For Coding (4-6 weeks):

  • 100-150 leetcode problems (mix of medium/hard)
  • Focus on graphs, trees, hash tables
  • Practice explaining your thought process
  • Write clean, commented code

For System Design (3-4 weeks):

  • Study feed/timeline systems (Twitter, Instagram, Facebook)
  • Learn about recommendation systems
  • Understand image storage and CDNs
  • Practice designing Pinterest-like features

For Behavioral (1-2 weeks):

  • Use Pinterest daily, note what works/doesn’t
  • Think about features you’d build
  • Prepare STAR stories showing data-driven thinking
  • Have opinions about product direction

Difficulty: 7.5/10

Similar to mid-tier FAANG. Easier than Google/Meta (9/10), harder than most Series B startups (6/10).

The coding is solid leetcode medium/hard. System design is where they really evaluate senior candidates.

Compensation (2024 data)

  • New grad: $150-170K base + $50-80K stock
  • Mid-level (3-5 YOE): $170-210K base + $80-140K stock
  • Senior (5-8 YOE): $210-280K base + $140-240K stock
  • Staff+: $300-450K+ total comp

Competitive with mid-tier FAANG. Stock vests over 4 years. 10-15% annual bonus.

Culture & Work Environment

Pros:

  • Interesting ML/computer vision problems
  • Positive, collaborative culture
  • Good work-life balance (45-50 hours/week)
  • Beautiful office (San Francisco)
  • Product people love the product
  • Remote-friendly post-COVID

Cons:

  • Slower growth than a few years ago
  • Some concern about long-term competitiveness
  • Not as much money as top-tier FAANG
  • Ad-driven revenue model (like most social)

Why I Didn’t Get The Offer

I did fine on coding rounds – solved both problems. System design was okay but I didn’t go deep enough on the ML aspects of recommendations. Behavioral was good.

Feedback: “Strong coding, but we wanted more depth on ML systems.” Fair – that wasn’t my strength at the time.

Tips for Success

  1. Actually use Pinterest: Browse, save Pins, understand the product
  2. Study recommendation systems: Comes up a lot in design rounds
  3. Practice medium/hard leetcode: They test similar difficulty to Meta
  4. Think about images at scale: Storage, CDN, optimization
  5. Be data-driven: Show you make decisions based on metrics
  6. Have product ideas: What would you build at Pinterest?

Resources That Helped

  • Pinterest Engineering Blog (medium.com/pinterest-engineering)
  • System Design Interview book (by Alex Xu)
  • Leetcode premium (for Pinterest-tagged questions)
  • Designing Data-Intensive Applications (Martin Kleppmann)

Pinterest is a solid choice if you want to work on interesting ML/recommendation problems at scale with better work-life balance than top-tier FAANG. The interview is fair but challenging – prepare thoroughly.

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