Snap·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026

Summary

Snap ML engineer interview, one question that basically asked you to think out loud about build-vs-buy tradeoffs for AI infrastructure. No idea how I did.

Questions Asked (1)

How would you decide whether to run an open-source model on your own infrastructure versus using a commercial API like OpenAI or Claude?

Technical Trade-offsSystem DesignProduct Strategy

Author's notes

I went straight to cost and latency, which felt right, but I forgot to talk about data privacy until the interviewer kind of nudged me toward it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the use case requirements—latency, cost, data privacy, and scale—then evaluate both options against those criteria. Structure your answer around a decision framework that weighs technical, operational, and business factors, and conclude with a recommendation that balances trade-offs.

Pro tip: Emphasize that the decision is not binary; you can use a hybrid approach where sensitive or high-volume tasks run on open-source models and others use APIs, optimizing for cost and performance.

1. Define Requirements and Constraints

Identify the specific task, expected query volume, latency needs, data sensitivity, and budget. This sets the criteria for evaluation.

2. Assess Total Cost of Ownership

Compare the costs of self-hosting (hardware, maintenance, engineering time) versus API pricing (per-token costs, scaling expenses). Consider both short-term and long-term scales.

3. Evaluate Performance and Customization

Benchmark open-source models against commercial APIs on your task. Consider fine-tuning needs, inference speed, and quality. Open-source offers more control but may require tuning.

4. Consider Operational and Strategic Factors

Weigh data privacy, compliance, vendor lock-in, and team expertise. Self-hosting gives data control but adds ops burden; APIs offer ease but may raise privacy concerns.

5. Decide and Iterate

Make a recommendation based on the analysis, and plan to revisit as needs change. Start with a pilot if uncertain.

Key Points to Mention

Data privacy and compliance: self-hosting keeps sensitive data in-house, crucial for user data at Snap.
Cost at scale: APIs are cheap for low volume but can become expensive at high scale; self-hosting has high fixed costs but lower marginal costs.
Latency and throughput: self-hosting can reduce network latency and offer predictable performance, but requires capacity planning.
Customization and control: open-source models allow fine-tuning and architectural changes, while APIs are black-box.
Operational overhead: self-hosting requires MLOps, monitoring, and maintenance; APIs abstract that away.
Hybrid approach: use APIs for prototyping or low-volume tasks and self-host for high-volume or sensitive workloads.

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