Shopify·Machine Learning Engineer·Technical Phone Screen·Intermediate

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Jun 2026Remote

Summary

Phone screen for a Machine Learning Engineer role at Shopify, pair programming format with AI tools allowed (they suggested Cursor). The problem was a URL shortener, similar to LeetCode 535, with some follow-ups tacked on. Not brutal, but communication mattered more than I expected.

Questions Asked (1)

Design and implement a URL shortening service (encode/decode), with follow-up extensions added by the interviewer.

System DesignAlgorithms & Data StructuresAPI & Integrations

Author's notes

This one shows up in previous reports so I wasn't totally blindsided.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, read/write ratio, custom aliases, expiration) and then design a scalable system using a unique ID generator (e.g., Snowflake) and base62 encoding. Discuss trade-offs of different approaches (hashing vs. counter) and be prepared to extend with analytics, rate limiting, or ML-driven features like spam detection or personalized short links.

Pro tip: For an ML Engineer role, proactively suggest how ML could enhance the service—e.g., predicting malicious URLs, optimizing cache eviction, or personalizing link recommendations—to show domain expertise and business impact.

1. Clarify Requirements and Scope

Ask about expected traffic (QPS), read/write ratio, latency requirements, custom aliases, expiration, and analytics needs. Confirm whether the service is internal or public, and discuss data retention and privacy constraints.

2. High-Level Design

Outline the core components: API gateway, encoding service, ID generator, database (SQL/NoSQL), cache (Redis), and analytics pipeline. Explain the flow for shortening and redirecting, and how to ensure uniqueness and scalability.

3. Deep Dive into Key Components

Detail the ID generation strategy (e.g., distributed counter, Snowflake, or hash with collision handling) and base62 encoding. Discuss database schema, indexing, and caching strategy (e.g., LRU, TTL). Address trade-offs between consistency and availability.

4. Handle Extensions and ML Integration

For follow-ups like analytics, rate limiting, or spam detection, propose ML solutions: e.g., a classifier for malicious URLs, anomaly detection for traffic spikes, or reinforcement learning for cache optimization. Explain how to integrate models into the serving path without adding latency.

5. Discuss Scalability, Reliability, and Monitoring

Cover horizontal scaling, database sharding, replication, and failover. Mention monitoring (latency, error rates, cache hit ratio) and how to handle hot keys. If ML models are used, discuss model versioning, retraining, and A/B testing.

Key Points to Mention

Unique ID generation strategies (e.g., Snowflake, distributed counters) and base62 encoding for short URLs.
Database choice (SQL vs. NoSQL) and schema design with indexing on short URL for fast lookups.
Caching layer (e.g., Redis) to reduce database load and improve read latency, with appropriate eviction policies.
Handling custom aliases and expiration: separate logic and storage considerations.
ML applications: spam/malware detection, traffic prediction for auto-scaling, and personalized link recommendations.
Trade-offs: consistency vs. availability, latency vs. accuracy of ML models, and cost implications.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.