Samsung Machine Learning Engineer Interview Process & Question Guide

Working at Samsung Electronics means joining one of the world’s most influential companies at the intersection of hardware and AI innovation. As a Samsung Machine Learning Engineer, you’ll contribute to cutting-edge projects—from mobile devices to advanced semiconductor systems—where algorithmic thinking and large-scale systems design are essential.

Whether you’re applying for a Machine Learning Engineer, AI Engineer, or another technical role, understanding the Samsung interview process is crucial. This guide will walk you through what to expect during the interview stages and how to prepare for your next Samsung Electronics interview.

Why Pursue a Machine Learning Engineer Role at Samsung?

Samsung offers one of the most exciting environments for machine learning engineers looking to work on real-world AI problems at scale. From personalized recommendations to smart device optimization, the applications of machine learning at Samsung are diverse and deeply embedded across its global product lines. Engineers here aren’t just building models—they’re shaping how next-generation technology learns, adapts, and delivers value in billions of devices.

Application & Recruiter Screen

The process often begins with an online application or outreach from a recruiter. During this initial screening, recruiters assess whether your resume aligns with Samsung’s focus areas—such as artificial intelligence, machine learning, embedded systems, and signal processing. Candidates may be asked a few preliminary questions about their background, geographic preference, and reasons for applying, to ensure basic alignment before moving forward.

Technical Interviews

Samsung’s technical interviews are designed to rigorously evaluate your computer science fundamentals and applied machine learning knowledge.

Coding Challenges are commonly delivered through platforms like HackerRank or LeetCode. You can expect problems covering topics like graph traversal, dynamic programming, and algorithmic optimization. For example, a typical prompt might involve solving a medium-level graph problem within 30 minutes.

ML/DL Questions dive into your understanding of both theoretical concepts and real-world implementation. Interviewers may probe your knowledge of LSTMs, the bias-variance tradeoff, CNN architectures, batch normalization, and challenges in model training and tuning. You’ll need to demonstrate fluency in both foundational ML theory and how it’s applied in practice.

Use of Past Projects is another important element. Candidates are frequently asked to present previous machine learning or deep learning projects, explain design choices, and justify model selection. You should be ready to defend trade-offs, discuss alternatives, and articulate lessons learned from your work.

Onsite / Final Round

The final stage typically involves multiple rounds with technical leads, hiring managers, or engineers. These may take place onsite or over video calls, depending on the team and location.

System Design or Architecture Discussions focus on your ability to architect solutions—especially in the context of machine learning systems. Topics include model deployment, data flow, and scalability. For roles tied to embedded systems or on-device ML, expect domain-specific design questions.

Behavioral Assessments are also conducted, either by HR or engineers. You’ll be evaluated on communication skills, teamwork, ownership, and how well you align with Samsung’s collaborative and high-performance culture.

Feedback & Offer

After all interview stages, a hiring panel evaluates your performance holistically. This includes your technical depth, ability to communicate complex ideas, and whether your goals and working style align with the team. Timelines for feedback and offers vary based on location and business unit—candidates interviewing for roles in cities like Amman, Bengaluru, or Moscow may experience different pacing due to team size or local processes.

Securing an ML Engineer role at Samsung means proving you can build robust, scalable machine-learning solutions that run smoothly on both cloud and edge devices, while also fitting into a fast-paced, innovation-driven culture. The interview is structured into three parts—Coding/Technical, System/Product Design, and Behavioral (“Culture Fit”)—to assess your hands-on programming skills, your ability to architect end-to-end ML systems, and your alignment with Samsung’s core values of collaboration, execution excellence, and customer-obsession.

Coding / Technical Questions

You’ll be challenged with algorithmic and data-structure problems in Python or C++, as well as ML-specific tasks like implementing core model components (e.g., decision trees, neural-network layers) from scratch. Expect questions on probability, linear algebra, and optimization techniques that underpin training and inference. You may also debug a snippet of model-serving code or write SQL to prepare features for downstream tasks. Demonstrating clean, efficient code and clear reasoning about time/space complexity is key.

1. Between linear regression and random forest regression for predicting booking prices, which would you choose and why?

   In a Samsung travel-app scenario, weigh interpretability (linear regression) against capturing non-linear patterns (random forests). Discuss data volume on our platform, tolerance for model size on embedded devices, and whether stakeholders need transparent pricing drivers. At Samsung, balancing a lightweight on-device model with cloud-offload capabilities often dictates the choice between simple and ensemble methods.

2. Given a perfectly linearly separable dataset, what would happen when you run logistic regression?

   Highlight that, without regularization, logistic regression’s weights will diverge toward infinity trying to maximize likelihood—an issue in clean benchmark datasets. Explain how adding an L2 penalty (via libraries like TensorFlow Lite or our in-house framework) stabilizes training and ensures convergence, a critical step when developing robust anomaly detectors for SmartThings sensors.

3. How would you interpret coefficients of logistic regression for categorical and boolean variables?

   In Samsung’s context—where models power features like Bixby intent detection or SmartThings anomaly alerts—you’ll need to explain how each coefficient translates into changes in log-odds for your target class. Describe interpreting odds ratios relative to a chosen baseline category for multi-class features and how a Boolean flag (e.g., “user is in portrait mode”) shifts the decision boundary. Emphasize the importance of clear communication when presenting these insights to product managers and firmware engineers who tune on-device inference thresholds.

4. What’s the difference between Lasso and Ridge Regression?

   When tuning models for image-processing features on Galaxy devices, controlling model complexity matters. Explain that Ridge applies an L2 penalty, shrinking weights smoothly to prevent overfitting without zeroing any feature, whereas Lasso’s L1 penalty can drive irrelevant feature weights to exactly zero—useful for on-device models where memory is constrained.

5. Given a list of timestamps in sequential order, return a list of lists grouped by week (7 days) using the first timestamp as the starting point.

   This question evaluates your ability to implement efficient time-window batching—critical for processing device telemetry or user interaction logs at Samsung scale. You might need to parse ISO-formatted strings into Python datetime objects (or use Pandas), then slide a fixed 7-day window to partition millions of events into weekly buckets. Focus on clear boundary handling, minimal memory overhead, and O(n) traversal of the list.

6. Given two strings, write a function to return True if the strings are anagrams of each other and False if they are not.

   String manipulation and hashing are foundational skills for feature-extraction tasks—such as deduplicating product descriptions in Samsung’s marketplace or normalizing voice commands in Bixby. Aim for an O(n) approach by counting character frequencies in a hash map rather than relying on O(n log n) sorting. Include edge-case checks for differing lengths and non-alphabetic characters if needed by the use-case.

7. Write a function that outputs the (sample) variance given a list of integers.

   Understanding and computing unbiased variance (with an n–1 denominator) is vital when validating model inputs or monitoring metric stability in Samsung’s ML platforms. Your implementation should handle trivial cases—empty or single-element lists—with appropriate error handling. Emphasize numerical stability, for example by using a two-pass algorithm or Welford’s method if precision matters on large integer ranges.

8. Given a list of friends with 3D coordinates (x, y, z), write a function pick_host to find the friend whose location minimizes total travel distance for the group to host a party. This problem tests your ability to apply geometric algorithms and optimization techniques—key skills for an MLE at Samsung working on location-based services or sensor data fusion. It assesses your handling of multidimensional data, distance metrics, and efficient search strategies, reflecting real-world tasks such as optimizing device connectivity or resource allocation across a network of devices.

9. Build a k-Nearest Neighbors classifier using Euclidean distance, capable of handling data frames with arbitrary size, and resolving ties by reducing k dynamically.

   This question probes your foundational understanding of classic ML algorithms and your ability to implement them from first principles—an essential skill for Samsung MLEs who often customize algorithms for embedded systems or optimize models for hardware constraints. It also tests your proficiency in numerical computing libraries and handling edge cases in classification problems.

10. Given a stream of numbers, implement a method to select a random number from the stream with equal probability using O(1) space.

    This question examines your grasp of reservoir sampling algorithms, which are crucial for processing large-scale data streams or sensor data on memory-limited devices—a typical scenario in Samsung’s IoT and mobile device ecosystems. It evaluates your ability to write efficient, online algorithms that can handle continuous input without storing the entire dataset.

System / Product Design Questions

Here you’ll design end-to-end ML pipelines: from data ingestion and feature engineering to model training, validation, deployment, and monitoring. Be prepared to choose between on-device inference versus cloud inference, discuss trade-offs in latency, accuracy, and resource constraints, and sketch a high-level architecture that includes data storage, feature stores, model registries, and CI/CD for models. Highlight how you’d instrument monitoring, set up A/B testing, and ensure models can be retrained and rolled back seamlessly in production.

1. Design a system to minimize wrong orders

   For Samsung’s e-commerce or parts-ordering platform, start by ingesting order events through Kafka or our internal message bus, then enrich them with inventory and user history in a feature store. Propose real-time ML scoring—using an XGBoost model deployed on-prem or in Samsung Cloud—to flag likely errors before fulfillment. Include feedback loops from delivery and customer-service APIs to retrain models continuously and reduce mis-picks over time.

2. How would you architect a federated learning pipeline for on-device personalization across Galaxy smartphones?

   At Samsung scale, you’d need a secure, privacy-preserving framework that coordinates model updates from millions of Galaxy devices back to Samsung Cloud. Start by defining a lightweight client SDK that performs local gradient computation on Exynos NPUs, encrypts updates, and ships only model deltas. On the server side, propose an aggregation service with differential-privacy guarantees and versioned model registries.

3. How would you build a scalable, multi-modal feature-extraction service for camera, sensor, and text data on Samsung devices?

   Begin by defining a unified feature schema and metadata catalog—perhaps leveraging a central registry in Samsung Cloud—for consistency across feature types. Propose microservices or edge-lets that perform on-device preprocessing (e.g., image embeddings via TensorFlow Lite, sensor normalization, NLP tokenization) and stream features to a central feature store.

4. Design an end-to-end data pipeline for collecting and processing health metrics from Samsung Health wearables, ensuring privacy and compliance.

   You’ll need a secure ingestion layer that transmits encrypted telemetry (heart rate, sleep, activity) from devices to Samsung Cloud over TLS. Outline the storage approach—using HIPAA-compliant data lakes or encrypted Db2 instances—and data-masking or anonymization at rest. Propose staging and processing via Spark or Flink jobs to normalize, validate, and aggregate metrics for downstream ML and visualization.

5. How would you integrate explainability and model-interpretability features into a real-time recommendation system on Samsung SmartThings?

   Users and product teams need to understand why an ML-driven suggestion (e.g., energy-saving thermostat adjustment) was made. Outline embedding a lightweight explainability library (SHAP or custom tree-based approach) into your inference service, capturing feature attributions alongside predictions. Design a side-channel API that surfaces human-readable explanations through the SmartThings app, balancing verbosity with performance constraints on mobile clients.

Behavioral or “Culture Fit” Questions

Samsung values teamwork, relentless execution, and user-centric innovation. Through STAR-style prompts, you’ll describe past experiences—such as leading a cross-functional ML project, overcoming ambiguity in research-to-product workflows, or handling a critical production incident. Focus on how you collaborated, made data-driven decisions under pressure, and delivered results that delight users. Clear storytelling, measurable outcomes, and reflections on what you learned will show you’re a strong cultural addition.

1. Tell me about a time when you exceeded expectations during a project. What did you do, and how did you accomplish it?

   At Samsung, going beyond the spec can unlock new product value—whether it’s squeezing extra accuracy out of an on-device NPU model or surfacing unexpected insights from sensor data. Describe how you identified the opportunity (e.g., a hidden user-behavior signal in SmartThings), challenged the original scope, and delivered a solution that outperformed targets—quantifying impact in terms of model lift, latency reduction, or feature adoption.

2. What strengths have helped you succeed as a data scientist in ambiguous projects? What feedback have you received that helped you grow?

   Thriving in ambiguity—such as spec’ing a voice-assistant feature with incomplete data—is key at Samsung. You might point to your ability to design rapid prototypes (e.g., a small-scale Bixby intent classifier) to validate assumptions before full development. For feedback, mention earlier advice to enhance cross-team communication or to formalize experiment tracking—then show how you built clearer documentation or CI/CD hooks to drive faster iterations and tighter alignment.

3. Describe a data project you worked on. What were some of the challenges you faced?

   Whether it was streaming health metrics from Galaxy Watches or real-time recommendation for Smart TV, Samsung ML projects span edge to cloud. Pick a project that involved heterogeneous data sources or tight latency SLAs, then walk through how you diagnosed issues—schema mismatches in your feature store, GPU-memory constraints during training, or flaky inference under heavy load. Detail how you collaborated with data engineers, firmware teams, and product owners to implement fixes and validate results end-to-end.

4. How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?

   In Samsung’s rapid-release cadence, ML Engineers may run model training, integrate with device firmware, and prep demos for trade shows—all at once. Explain your decision-framework: ranking tasks by user-impact, risk, and dependencies; chunking work into daily objectives; and using tools like Kanban boards or shared OKR trackers. Emphasize proactive status updates, adaptive planning when new data arrives, and how you ensure code quality (peer reviews, unit tests) even under pressure.

5. Tell me about a time you received tough feedback. What was it, and what did you do afterward?

   At Samsung, constructive feedback often centers on on-device performance or cross-functional communication—key to delivering polished ML features on Galaxy devices. Describe a situation where a peer or product manager flagged an issue, for example, your TensorFlow Lite model’s inference latency exceeding NPU constraints or unclear experiment reports delaying decision-making. Explain how you internalized that feedback: perhaps you profiled and optimized your model graph, refactored code for better maintainability, or instituted regular syncs with firmware teams.

To succeed in a Samsung ML Engineer interview, you’ll need more than strong coding skills—you must also understand model deployment on constrained devices, cross-team collaboration, and real-world ML applications in consumer electronics. Here’s how to strategically prepare.

Understand the Role & Domain

Start by building strong fundamentals in machine learning, including model fine-tuning, deployment, and the ability to explain models clearly to non-technical stakeholders. Familiarize yourself with common neural network architectures such as CNNs, LSTMs, and Transformers, along with core optimization techniques. If you’re targeting roles in mobile or IoT, understand the constraints of embedded systems—such as limited memory, power, and latency—and study edge AI methods. Refresh your knowledge of foundational math concepts like probability, statistics, linear algebra, and calculus, as these often underlie ML model behavior and decisions.

Practice Technical Skills

Sharpen your coding and algorithmic thinking through platforms like LeetCode or HackerRank, especially focusing on problems tagged “Samsung.” Prioritize topics such as graphs, trees, dynamic programming, and low-level implementation challenges. Go beyond basic algorithms by learning ML-specific system design and object-oriented programming patterns—for example, implementing an LRU cache or designing scalable model-serving pipelines. These skills are vital for building efficient ML systems in resource-constrained environments.

Use Real-World Product Context

Tailor your answers to Samsung’s actual products and use cases—such as SmartThings, Bixby, or Galaxy AI features. Discuss trade-offs in model design that consider real-world constraints like inference time, energy efficiency, and hardware limitations. Demonstrating awareness of product constraints and user-centric design choices helps show that you can deliver practical, deployable ML solutions in consumer electronics.

Mock Interviews & Storytelling

Practice behavioral and design questions using the STAR method (Situation, Task, Action, Result) to communicate your work clearly and effectively. Be prepared to walk interviewers through your end-to-end ML projects, explaining key decisions, trade-offs, and lessons learned. Clear storytelling not only shows technical understanding but also your ability to collaborate and lead in team settings.

Research Samsung’s Ecosystem

Take time to explore Samsung Research, Samsung Next, and their portfolio of AI-enabled products like Galaxy AI. Understand the company’s direction in large language models (LLMs), generative AI, and multimodal learning. Highlight your passion for innovation and your ability to contribute to cross-functional teams that align with Samsung’s AI vision and product goals.

Candidates often wonder about interview format, technical expectations, and role-specific challenges at Samsung. This section answers the most common questions aspiring Machine Learning Engineers have before and during the Samsung interview process.

What Is the Average Salary for a Machine Learning Engineer at Samsung?

Are There Job Postings for Samsung Roles on Interview Query?

Yes! Visit our Interview Query Jobs Board and search “Samsung” for the latest openings and referrals.

Why Pursue a Machine Learning Engineer Role at Samsung?

Samsung offers one of the most exciting environments for machine learning engineers looking to work on real-world AI problems at scale. From personalized recommendations to smart device optimization, the applications of machine learning at Samsung are diverse and deeply embedded across its global product lines. Engineers here aren’t just building models—they’re shaping how next-generation technology learns, adapts, and delivers value in billions of devices.

To succeed as a Samsung Machine Learning Engineer, it’s important to be confident in core skills like machine learning algorithms, data preprocessing, system design, Python, SQL, and working with cloud platforms. These are key to building scalable and reliable ML solutions in real-world settings.

When preparing, focus on the fundamentals—like supervised vs. unsupervised learning, model tuning, and evaluation. Practice coding and SQL regularly, and get comfortable with system design basics. Mock interviews can help you improve how you explain your work. Most importantly, tailor your prep around practical ML problems, especially ones that match Samsung’s products and tech goals.

If you’re interviewing at Samsung, you’re likely applying for a highly technical, global-facing role with product impact across millions of devices. Preparation is key. Use our Machine Learning Interview Learning Path to structure your study plan, review real ML interview questions, and get inspired by Dania’s success story. Interview Query has the tools to help you prep smarter and land the role.