Scale.ai·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Scale.ai Forward Deployed Engineer interview, AI coding round done in a Google Colab notebook. The task was filling in chunks of Python code to run LLM evaluations, about an hour total. Pretty practical and hands-on compared to your typical leetcode grind.

Questions Asked (1)

Complete the missing sections of a Python notebook to implement LLM evaluation logic.

API & IntegrationsTechnical Trade-offs

Author's notes

Worked through it in Colab, filling in the blanks across a few code cells.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the evaluation goals and metrics, then implement modular functions for data loading, model inference, and metric computation. Use a configuration-driven design to easily swap models and metrics, and validate with a small test set before scaling.

Pro tip: Write unit tests for each evaluation component and log intermediate results to catch issues early, especially when dealing with API rate limits or inconsistent model outputs.

1. Clarify Requirements and Metrics

Ask clarifying questions about the evaluation task, dataset, and desired metrics (e.g., accuracy, F1, BLEU). Confirm the expected input/output formats and any constraints like API rate limits.

2. Design Modular Components

Outline separate functions for loading data, calling the LLM API, parsing responses, and computing metrics. Ensure each component is testable and configurable.

3. Implement Data Loading and Preprocessing

Write code to load the evaluation dataset, handle missing values, and format inputs as required by the LLM. Include error handling for malformed data.

4. Implement LLM Inference and Response Parsing

Create a function to call the LLM API with retries and rate limiting, then parse the raw responses into a structured format suitable for metric computation.

5. Compute Metrics and Report Results

Implement metric calculations, aggregate results, and output a summary report. Include logging and visualization if needed.

Key Points to Mention

Modular design for reusability and testing
Handling API rate limits and retries
Data validation and error handling
Choice of evaluation metrics and their trade-offs
Configuration management for different models/datasets
Logging and reproducibility

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