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.

Preparation for AMD requires a shift in mindset. While you need strong general ML knowledge, you must also demonstrate an understanding of how these algorithms translate to computation. Do not just prepare to explain what a model does; prepare to explain how it runs.

Technical Depth & Low-Level Optimization – 2–3 sentences describing: At AMD, abstract knowledge is rarely enough. Interviewers will evaluate your understanding of matrix operations, memory hierarchy (cache vs. global memory), and computational complexity. You must demonstrate the ability to identify bottlenecks in ML pipelines and propose architectural or code-level solutions to resolve them.

Implementation Rigor – 2–3 sentences describing: You will likely be asked to implement ML components from scratch rather than relying solely on high-level APIs. Evaluation focuses on your ability to write clean, efficient code (often in C++ or Python) that handles the mathematical logic of layers like Batch Normalization or Attention mechanisms explicitly.

Problem-Solving in Ambiguity – 2–3 sentences describing: AMD operates in a fast-paced environment where software stacks are constantly evolving. Interviewers look for candidates who can navigate undefined problems, such as debugging performance regressions in a distributed training setup or porting a custom kernel to a new architecture without a perfect roadmap.

The interview process at AMD is rigorous and technical, often starting earlier than candidates expect. After your resume is selected, you may encounter an initial screening with a recruiter or, quite frequently, a direct screening with a Hiring Manager. Be aware that even this initial conversation can turn technical quickly. Candidates have reported hiring managers diving into high-level technical discussions regarding your past projects and testing strategies within the first 30 minutes, so ensure you are technically sharp from the very first call.

Following the screens, successful candidates move to a series of technical rounds. These can be split into multiple virtual sessions or a consolidated onsite loop. Expect a mix of coding interviews, deep-dive theory sessions, and system design discussions tailored to ML infrastructure. The focus often shifts between theoretical understanding (e.g., the math behind transformers) and practical optimization (e.g., how to speed up matrix multiplication). The process is designed to filter for engineers who possess both the theoretical background of a researcher and the practical skills of a systems engineer.

This timeline illustrates the typical progression from application to offer. Use this to pace your study schedule; the gap between the technical screen and the final rounds is your critical window for deep technical review. Note that for specialized teams (such as those working on ROCm or GPU kernels), the "Technical Screen" phase may involve specific questions on computer architecture.

AMD’s evaluation is highly specific to the hardware-software co-design nature of the business. Based on recent candidate experiences, you should prioritize the following areas.

ML Kernels and Low-Level Optimization

This is the most critical differentiator for AMD interviews. You are expected to understand how ML operations map to hardware. It is not enough to know that a matrix multiplication happens; you need to understand how it is parallelized.

Be ready to go over:

• GEMMs (General Matrix Multiply) – Understand the mechanics of matrix multiplication, tiling strategies, and memory coalescing.
• Memory Hierarchy – Explain the difference between HBM, L2 cache, and registers, and how to optimize data movement for bandwidth-bound kernels.
• Kernel Fusion – Discuss why fusing operations (like Add + ReLU) improves performance by reducing memory access overhead.
• Advanced concepts – Knowledge of Triton, CUDA/HIP programming, or specific optimizations for Flash Attention.

Example questions or scenarios:

• "How would you optimize a matrix multiplication algorithm for a GPU?"
• "Describe the bottlenecks in a standard Transformer layer. Is it compute-bound or memory-bound?"
• "How do you handle thread divergence in a parallel computing environment?"

Machine Learning Theory & Implementation

Interviewers will verify that you understand the "magic" behind the libraries. You may be asked to implement common layers without using torch.nn.

Be ready to go over:

• Normalization Layers – The math and implementation details of Batch Norm, Layer Norm, and RMSNorm.
• Attention Mechanisms – The specific mathematical operations in Self-Attention and Multi-Head Attention, including complexity analysis.
• Backpropagation – Deriving gradients for custom layers manually.

Example questions or scenarios:

• "Write code to implement Batch Normalization from scratch (forward and backward pass)."
• "Explain the concept of Lean Attention and how it differs from standard attention mechanisms."
• "Walk me through the implementation details of your research paper."

System Design & Testing

For senior roles, evaluation extends to how you design robust systems and verify their correctness. AMD emphasizes testing because hardware bugs or compiler errors can be subtle and catastrophic.

Be ready to go over:

• Testing Strategies – Unit testing, integration testing, and numerical correctness verification (e.g., comparing your kernel output against a reference implementation).
• Distributed Training – Data parallelism vs. model parallelism vs. pipeline parallelism.
• Debugging – Strategies for isolating performance regressions.

Example questions or scenarios:

• "How would you verify that a new GPU kernel is numerically accurate compared to the CPU implementation?"
• "Design a testing strategy for a new feature in the ROCm stack."

The word cloud above highlights the frequency of technical terms in recent interviews. Notice the prominence of terms like Matrix Multiplication, GEMM, Optimization, and Implementation. This signals that while general ML concepts are present, the "center of gravity" for this interview is clearly on the implementation and optimization side. Prioritize your study time accordingly.

As a Machine Learning Engineer at AMD, your day-to-day work revolves around bridging the gap between state-of-the-art AI models and AMD’s hardware ecosystem. You will be responsible for analyzing the performance of neural networks and identifying bottlenecks that prevent them from running efficiently on Instinct GPUs or Ryzen AI processors. This often involves profiling workloads to understand memory access patterns and compute utilization.

You will collaborate heavily with hardware architects, compiler engineers, and library developers. A typical project might involve taking a new open-source model (like Llama or Stable Diffusion), profiling it on AMD hardware, and writing custom kernels or optimizing existing libraries (like MIOpen) to improve throughput. You are also expected to contribute to the ROCm software stack, ensuring that the developer experience on AMD hardware is seamless and competitive.

Beyond coding, you will likely engage in "whiteboarding" solutions for future hardware generations. Your feedback on current bottlenecks helps influence the design of future chips. You will also develop testing suites to ensure that optimizations do not introduce numerical instability, maintaining the high reliability required for enterprise AI workloads.