What is a Machine Learning Engineer at AMD?

At AMD, the Machine Learning Engineer role is distinct from typical data science positions at software-first companies. Here, you sit at the critical intersection of advanced software and cutting-edge hardware. You are not just building models; you are defining how the next generation of AI workloads runs on AMD’s Instinct accelerators, Ryzen AI processors, and the ROCm open software platform. This role is fundamental to AMD’s strategy of challenging the status quo in high-performance computing and generative AI.

You will work on enabling and optimizing deep learning frameworks (like PyTorch, TensorFlow, and JAX) to extract maximum performance from AMD silicon. This often involves diving deep into kernel optimization, compiler technologies, and distributed training strategies. Your work directly impacts how fast large language models (LLMs) train and infer, influencing the product capabilities of major cloud providers and enterprise partners who rely on AMD infrastructure.

This position offers a unique opportunity to work "under the hood" of modern AI. You will tackle complex challenges related to memory bandwidth, compute efficiency, and parallel processing. If you are passionate about squeezing every ounce of performance out of hardware and understanding the full stack—from the transistor to the transformer—this role is designed for you.

Getting Ready for Your Interviews

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.

Interview Process Overview

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.

Deep Dive into Evaluation Areas

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.

Key Responsibilities

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.

Role Requirements & Qualifications

Candidates who succeed at AMD typically possess a blend of strong mathematical foundations and systems engineering skills.

• Technical Skills

  • Must-have: Proficiency in C++ and Python. Deep understanding of PyTorch or TensorFlow internals (not just high-level usage). Experience with GEMMs, linear algebra, and performance profiling tools.
  • Nice-to-have: Experience with CUDA, HIP, or OpenCL. Knowledge of compiler technologies (MLIR, TVM, Triton). Familiarity with LLM architectures (Transformers, MoE).

• Experience Level

  • Candidates often hold a Master’s or PhD in Computer Science, Electrical Engineering, or Mathematics.
  • For non-intern roles, prior experience in performance optimization, high-performance computing (HPC), or kernel programming is highly valued.

• Soft Skills

  • Ability to communicate complex technical concepts to cross-functional teams (e.g., explaining a software constraint to a hardware engineer).
  • Strong self-direction; the ability to research and solve problems where documentation may be sparse or evolving.

Common Interview Questions

The following questions are representative of what you might face. They are drawn from recent candidate data and are designed to test both your theoretical knowledge and your practical engineering capability. Do not memorize answers; instead, use these to practice your problem-solving approach.

Low-Level Optimization & Math

This is the core of the AMD interview. Expect follow-up questions regarding complexity and hardware constraints.

• How do you optimize matrix multiplication for large matrices?
• Explain the concept of tiling and how it affects cache usage.
• What is the difference between memory-bound and compute-bound operations? Give an example of each in a Transformer model.
• How would you implement a 2D convolution operation from scratch efficiently?

Coding & Implementation

You will be asked to write code. The focus is often on implementing ML algorithms rather than generic LeetCode puzzles.

• Implement Batch Normalization (forward pass) in Python/NumPy without using a framework.
• Write a function to perform Softmax and discuss how to make it numerically stable.
• Implement a specific loss function (e.g., Cross-Entropy) and derive its gradient.

Behavioral & Experience

AMD values engineers who can navigate technical challenges and work collaboratively.

• Describe a time you optimized a piece of code. what tools did you use and what was the percentage improvement?
• Tell me about the most technically challenging bug you have faced in your research or work.
• How do you approach learning a new codebase or technology stack with limited documentation?
• Describe your research work implementation details. Why did you choose that specific architecture?

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.

Frequently Asked Questions

Q: How much hardware knowledge do I really need? You do not need to be a hardware architect, but you must understand the software-hardware interface. Knowing how data moves from memory to compute units, what causes latency, and basic GPU architecture (SIMD/SIMT) is essential for this role.

Q: Is the coding round LeetCode-style? It is a mix. While you may encounter standard algorithmic questions, AMD leans heavily toward domain-specific coding tasks. You are more likely to be asked to "implement a layer" or "optimize a math function" than to invert a binary tree.

Q: What is the biggest challenge candidates face in this interview? The depth of the "Why." Many candidates can explain what an algorithm does, but fail to explain why it is implemented that way or how to make it run faster. The inability to discuss memory complexity and computational cost is a common stumbling block.

Q: How long is the process? The process typically takes 3 to 5 weeks from initial contact to offer. However, this can vary based on team availability and the specific hiring cycle.

Other General Tips

Review Linear Algebra and Calculus: Refresh your memory on matrix operations, gradients, and chain rule derivations. You may be asked to derive backpropagation for a layer on a whiteboard.

Understand the "Why" behind AMD: Be prepared to discuss why you want to work on the AMD software stack specifically. Mentioning an interest in open ecosystems (ROCm) or the challenge of competing in the accelerator market shows alignment with their mission.

Know Your Research: If you have a PhD or research background, expect granular questions about your implementation. Interviewers will dig into how you implemented your experiments, not just the results you achieved.

Brush up on C++: While Python is the language of ML, C++ is the language of performance. Being comfortable reading and writing C++ (even if not perfect) is a significant advantage and often a requirement for kernel-level work.

Summary & Next Steps

The Machine Learning Engineer role at AMD is a premier opportunity for engineers who refuse to treat hardware as a black box. You will be challenged to push the boundaries of performance, contributing to a hardware ecosystem that powers some of the world's most advanced supercomputers and AI clusters. This is a role for builders, optimizers, and deep thinkers.

To succeed, focus your preparation on the intersection of math and metal. Master the implementation details of neural networks, understand the fundamentals of GPU architecture, and practice explaining complex optimizations clearly. If you can demonstrate that you understand not just the model, but the machine it runs on, you will be a standout candidate.

The compensation data above reflects the competitive nature of this specialized field. Note that for roles involving specialized kernel optimization or compiler work, compensation can scale significantly based on your ability to impact core infrastructure performance.

Good luck. Prepare deeply, focus on the fundamentals, and be ready to show AMD how you can help build the future of high-performance AI.