Netflix·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jul 2026Remote

Summary

Netflix ML infra screen, one coding question the whole time. The problem was deceptively clean on the surface but the rotation constraint is where things get interesting. Felt okay about my solution but never heard back on the outcome.

Questions Asked (1)

Design a service with two API methods: one to add or update a title's relevance score, and one to return the top title for a user's homepage. The catch is that the same title can't be returned on two consecutive calls if any other title exists.

Algorithms & Data StructuresAPI & IntegrationsSystem Design

Author's notes

The score ordering part is easy, just a heap or sorted structure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design the API contracts and data model. Propose an efficient algorithm using a max-heap or sorted structure to track top titles, and handle the no-consecutive-repeat constraint by tracking the last returned title and temporarily excluding it. Discuss trade-offs, concurrency, and scalability.

Pro tip: Mention that the 'no consecutive repeat' constraint can be elegantly handled by maintaining a cache of the last returned title and, if it matches the top, returning the second-best; this avoids complex state management. Also, emphasize idempotency and atomicity for the update operation.

1. Clarify Requirements and Scale

Ask about expected QPS, number of titles, update frequency, and whether the top title should be personalized per user. Confirm if the constraint applies per user or globally.

2. Define API Contracts

Specify request/response formats, error handling, and idempotency for the update method. For the get method, define parameters like user ID and response containing the top title.

3. Design Data Model and Storage

Choose a data store (e.g., in-memory cache, Redis, or database) that supports fast updates and reads. Consider using a max-heap or sorted set to maintain top titles efficiently.

4. Handle the No-Consecutive-Repeat Constraint

Track the last returned title per user (or globally). When fetching the top title, if it equals the last returned, return the next best title instead.

5. Address Scalability and Concurrency

Discuss sharding, replication, and locking strategies to handle concurrent updates and reads. Ensure the constraint is enforced atomically.

Key Points to Mention

Use of a max-heap or sorted set for efficient top-title retrieval
Tracking last returned title to enforce the no-consecutive-repeat rule
Idempotency and atomicity for the update operation
Caching strategies for low-latency reads
Concurrency control (e.g., optimistic locking) for updates
Trade-offs between consistency and availability (CAP theorem)

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