Design Work Orchestration for Machine-Learning Data Pipelines

Quick Overview

Design orchestration for versioned machine-learning data workflows built as task DAGs. Cover durable scheduling, retries, worker loss, lineage, reproducibility, validation gates, atomic dataset publication, backfills, resource-aware compute pools, tenant fairness, and prevention of invalid training data.

Design Work Orchestration for Machine-Learning Data Pipelines

Company: Mercor

Role: Machine Learning Engineer

Category: ML System Design

Difficulty: medium

Interview Round: Onsite

Overview: Design orchestration for versioned machine-learning data workflows built as task DAGs. Cover durable scheduling, retries, worker loss, lineage, reproducibility, validation gates, atomic dataset publication, backfills, resource-aware compute pools, tenant fairness, and prevention of invalid training data.

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Mercor
Jul 8, 2026
mediumMachine Learning EngineerOnsiteML System Design
45
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