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Outline the ML inference and labeling pipeline

RobloxSoftware Engineer · Onsite
HardSoftware EngineerOnsiteML System Design

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Outline the ML inference and labeling pipeline evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

For the audio detection system, outline the ML inference and data pipeline while keeping model architecture out of scope. Describe feature extraction choices (speech-to-text, spectrogram/MFCCs), keyword spotting, and denoising; how outputs are scored and thresholded; how you calibrate confidence and handle class imbalance; the contract for model inputs/outputs and storage of transcripts, embeddings, and intermediate artifacts; how manual labels are generated and fed back for active learning; and how you detect drift and manage versioning of models and thresholds.