Samsung·Machine Learning Engineer·Onsite - System Design / Architecture·Intermediate

Intermediate

Apr 2026

Summary

Samsung ML engineer interview with a system design question around waste sorting using computer vision. Pretty applied, less abstract than I expected from a big tech company.

Questions Asked (1)

Design a system that automatically detects paper waste and sorts it into the correct bin.

System DesignTechnical Trade-offs

Author's notes

I jumped straight into the ML model part (image classification, maybe object detection) and spent way too long there before realizing they probably wanted the full pipeline: camera setup, inference latency, bin actuation, edge vs cloud tradeoff.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints (e.g., scale, latency, accuracy, cost) and then propose an end-to-end system architecture that combines computer vision for waste detection with a robotic sorting mechanism. Emphasize the ML pipeline, including data collection, model training, deployment, and continuous improvement, while discussing trade-offs between accuracy, speed, and cost.

Pro tip: Demonstrate awareness of real-world deployment challenges such as lighting variations, occlusions, and the need for low-latency inference on edge devices. Propose a hybrid approach that combines a lightweight model for initial detection with a more complex model for fine-grained classification to balance speed and accuracy.

1. Clarify Requirements and Constraints

Ask questions to understand the scale (e.g., number of bins, throughput), accuracy requirements, latency constraints, budget, and environmental conditions. This ensures the design meets the actual needs.

2. High-Level System Architecture

Outline the main components: sensors (cameras), compute unit (edge or cloud), ML models for detection and classification, and actuators (robotic arm or conveyor system) for sorting. Describe the data flow from image capture to bin placement.

3. Machine Learning Pipeline

Detail the ML pipeline: data collection and labeling, model selection (e.g., CNN for classification, object detection for localization), training, evaluation, and deployment. Discuss techniques for handling class imbalance and domain adaptation.

4. Trade-offs and Optimization

Discuss trade-offs between model complexity, inference speed, and accuracy. Propose optimizations such as model quantization, pruning, or using specialized hardware (e.g., TPU, edge GPU) to meet latency and cost constraints.

5. Deployment and Monitoring

Explain how the system will be deployed (e.g., on-premise, edge devices) and monitored for performance drift. Include a feedback loop for continuous learning and model updates.

Key Points to Mention

Data collection and labeling strategy, including synthetic data and augmentation to handle rare classes.
Choice of model architecture (e.g., YOLO for detection, ResNet for classification) and justification based on accuracy and speed.
Edge vs. cloud inference: latency, privacy, and cost considerations.
Integration with robotic sorting mechanism and control system.
Evaluation metrics (e.g., precision, recall, F1, mAP) and how to handle false positives/negatives.
Scalability and maintenance: how to update models and handle multiple bin types.

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