These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Preparing for an interview at AMD requires a shift in mindset from pure software application development to a focus on performance, optimization, and hardware-software co-design. You need to demonstrate not only that you can write code, but that you understand how that code executes.

You will be evaluated on the following key criteria:

Technical Proficiency & Algorithmic Thinking – You must demonstrate strong coding skills, typically in C++ and Python. Interviewers look for clean, efficient code and the ability to solve LeetCode Medium style problems. Beyond syntax, you need to show you can handle data structures and algorithms under time constraints.

Domain Knowledge (AI/ML & Hardware) – Expect deep dives into machine learning concepts. You should understand the lifecycle of a model—from training to inference—and possess a conceptual understanding of how GPUs process these workloads. Familiarity with ROCm, HIP, or CUDA is often a significant differentiator.

Problem Structuring & Logic – AMD interviewers often prize your "train of thought" over a perfect immediate answer. They want to see how you break down a vague technical scenario—such as developing test cases for a new driver or debugging a performance bottleneck—and develop multiple solution paths.

Adaptability & Collaboration – The AI landscape at AMD is fast-paced and collaborative. You will be assessed on your ability to communicate complex technical ideas to cross-functional teams and your willingness to navigate the ambiguity of developing software for evolving hardware.

The interview process for the AI Engineer role generally follows a structured yet adaptable path. It typically begins with a recruiter screen to align on your background and logistics, followed by a technical phone screen or online assessment. If you pass this stage, you will move to a series of virtual onsite interviews. Candidates often report the process as medium difficulty, balancing standard coding challenges with domain-specific inquiries.

What makes the AMD process distinctive is the emphasis on spontaneous technical discussions. Rather than sticking strictly to a script, hiring managers often pivot the conversation based on your responses to test your depth of understanding. You might start discussing a standard ML algorithm and end up whiteboard-ing how to debug a specific kernel issue. The atmosphere is generally described as positive and professional, with interviewers genuinely interested in your thought process.

This timeline illustrates the typical progression from your first point of contact to a final decision. Use this to pace your preparation: focus on high-level behavioral and resume stories for the initial stages, then shift heavily into coding and ML theory before the technical screens and onsite rounds. Note that the duration can vary; while some candidates experience a swift process, others may see gaps between rounds depending on team availability.

To succeed, you must prepare for a mix of traditional software engineering questions and specialized AI/systems topics. Based on recent candidate experiences, you should focus your energy on these three major areas.

5. Data Structures and Algorithms

This is the baseline for the interview. You will likely face a coding round that involves a LeetCode Medium problem. The goal is to verify your coding fluency and ability to write bug-free code.

Be ready to go over:

• Arrays and Strings – Sliding window techniques, two pointers, and manipulation.
• Trees and Graphs – Traversal algorithms (BFS/DFS) are common for testing logical structuring.
• Dynamic Programming – Basic optimization problems often appear.
• Advanced concepts – While less common, be prepared for bit manipulation questions, as they are relevant to hardware-centric roles.

Example questions or scenarios:

• "Find the longest substring without repeating characters."
• "Given a binary tree, implement a function to validate if it is a Binary Search Tree (BST)."
• "Solve a specific array manipulation problem efficiently, then discuss the time and space complexity."

2. AI/ML Fundamentals and Optimization

Since this is an AI Engineering role, you must have a solid grasp of modern machine learning architectures and how they are deployed.

Be ready to go over:

• Deep Learning Concepts – Transformers, CNNs, backpropagation, and loss functions.
• Frameworks – How PyTorch or TensorFlow handles computation graphs.
• Model Optimization – Quantization, pruning, and inference acceleration.
• Advanced concepts – Knowledge of ROCm (AMD’s open platform for GPU computing) and how it compares to CUDA.

Example questions or scenarios:

• "Explain the difference between training and inference in the context of hardware utilization."
• "How would you optimize a Transformer model for deployment on an edge device?"
• "Discuss the vanishing gradient problem and how modern architectures mitigate it."

3. Systems and Scenario-Based Problem Solving

This area tests your engineering maturity. Interviewers often present open-ended scenarios to see how you approach testing, debugging, and system design.

Be ready to go over:

• Debugging Logic – How you isolate issues in a complex software stack.
• Testing Strategies – Developing test cases for drivers, kernels, or new features.
• Hardware Interaction – Understanding memory hierarchy and parallel computing basics.

Example questions or scenarios:

• "Develop a set of solution paths for creating test cases for a new ROCm feature."
• "How would you handle a situation where a model performs well in PyTorch but fails when converted to run on AMD hardware?"
• "Walk me through your thought process when a specific technical scenario fails spontaneously."

The word cloud above highlights the most frequently cited topics in AMD interview reports. You will notice a strong emphasis on Optimization, C++, Python, and Testing. This confirms that while theoretical ML knowledge is important, the ability to apply that knowledge to build robust, optimized software is the primary evaluation metric.

As an AI Engineer at AMD, your daily work revolves around enabling the next generation of AI applications on AMD hardware. You are responsible for developing and optimizing the software stack that allows developers to run AI workloads seamlessly. This often involves working with the ROCm platform, ensuring parity and performance competitiveness with alternative ecosystems.

You will collaborate closely with hardware architects, driver teams, and open-source communities. A significant part of the role involves analyzing performance bottlenecks—identifying whether a slowdown is due to the model architecture, the software framework, or the hardware execution itself. You may also be tasked with creating robust test suites to ensure stability across different GPU generations, or helping key partners migrate their codebases to the AMD stack.