1. What is a Machine Learning Engineer at Advanced Micro Devices?

At Advanced Micro Devices (AMD), the role of a Machine Learning Engineer is fundamentally about bridging the gap between cutting-edge AI software and high-performance hardware. Unlike generalist ML roles that focus solely on model architecture or data cleaning, this position at AMD is deeply entrenched in hardware-software co-design. You are not just training models; you are defining how the next generation of Generative AI, Large Language Models (LLMs), and computer vision systems run on AMD’s instinct accelerators (such as the MI300 series) and consumer GPUs.

This role is critical to AMD’s strategic mission to challenge the status quo in the AI accelerator market. You will work within teams like the Models and Applications team, the Llama team, or the Advanced Graphics Program. Your impact is measured by your ability to optimize distributed training pipelines, enhance the ROCm open software ecosystem, and push the performance boundaries of frameworks like PyTorch, JAX, and TensorFlow. You are the engineer ensuring that the world's most complex AI workloads run efficiently and at scale on AMD silicon.

Candidates joining AMD in this capacity enter an environment that values engineering rigor and "underdog" innovation. You will tackle complex problems involving distributed systems, kernel optimization, and massive-scale cluster management. Whether you are optimizing inference for Agentic AI or pushing the limits of real-time neural graphics, your work directly empowers developers and researchers to choose AMD as their platform of choice for the AI revolution.

2. Getting Ready for Your Interviews

Preparing for an interview at AMD requires a shift in mindset from "how do I build this model?" to "how does this model execute on the metal?" You need to demonstrate a strong grasp of the entire stack, from the high-level Python framework down to the C++ runtime and GPU memory hierarchy.

Hardware-Aware Problem Solving You must demonstrate an understanding of how software interacts with hardware. Interviewers evaluate whether you understand concepts like memory bandwidth, latency hiding, and compute utilization. You should be able to explain not just why a model works, but how to make it run faster on a GPU cluster.

Low-Level Engineering Proficiency AMD places a high premium on strong C++ and Python skills. Unlike pure data science roles, you will likely be tested on C++ pointers, memory management, and debugging complex system-level issues. You need to show that you can dig into the source code of frameworks like PyTorch or DeepSpeed to fix bottlenecks.

Distributed Systems Knowledge For ML Infrastructure and Training roles, you are evaluated on your knowledge of scaling strategies. You must be comfortable discussing Data Parallelism (DP), Tensor Parallelism (TP), Pipeline Parallelism (PP), and ZeRO optimization stages. Success here means understanding the communication overheads (NCCL/RCCL) involved in multi-node training.

Adaptability and Open Source Contribution AMD champions an open ecosystem (ROCm). Interviewers look for candidates who are adaptable—perhaps you know CUDA well but are eager to master ROCm/HIP. They value engineers who contribute to open source and can navigate the ambiguity of evolving software stacks.

3. Interview Process Overview

The interview process at AMD is thorough and technically rigorous, designed to assess both your fundamental engineering skills and your specialized domain knowledge. Generally, the process begins with a recruiter screen to align on your background and interest in specific teams (e.g., AI Infra, Model Performance, or Applied Research). This is followed by one or two technical phone screens. These screens often involve coding challenges (LeetCode style, often focused on arrays, strings, or pointers) and a discussion on basic ML or computer architecture concepts.

If you pass the screening stage, you will move to the "Onsite" loop (usually virtual). This typically consists of 4 to 5 separate interviews, each lasting 45–60 minutes. You can expect a mix of coding rounds, deep-dive system design sessions, and behavioral interviews. For senior or principal roles, you may be asked to present a past project or research paper to a panel of engineers, followed by a Q&A session. This presentation is a critical opportunity to showcase your depth in performance optimization or distributed training.

AMD’s interviewing philosophy leans heavily on technical practicality. While they value theoretical knowledge, they are more impressed by candidates who can discuss the trade-offs of specific implementation choices, such as why you would choose a specific quantization technique or how you debugged a race condition in a distributed workload. The atmosphere is generally collaborative; interviewers want to see how you think and how you handle technical debate.

The timeline above represents a typical flow, though the specific technical focus of the "Onsite" rounds will vary based on the team (e.g., the Graphics team may focus more on rendering pipelines, while the Infra team focuses on Kubernetes and MPI). Use the time between the phone screen and the onsite to refresh your knowledge of GPU architecture and C++, as these are high-failure points for many candidates.

4. Deep Dive into Evaluation Areas

To succeed, you must prepare for specific technical domains that define AMD's engineering challenges.

GPU Architecture and Performance Optimization

This is the differentiator for AMD interviews. You need to understand how GPUs process data and where bottlenecks occur.

• Memory Hierarchy: Registers, shared memory, L1/L2 caches, and HBM (High Bandwidth Memory). Be ready to discuss memory coalescence.
• Kernel Optimization: Understanding SIMD execution, warps/wavefronts, and occupancy.
• Profiling: Experience with tools like Nsight Systems (or AMD's rocprof) to identify stalls.
• Advanced concepts: Kernel fusion, operator tiling, and writing custom kernels in Triton, CUDA, or HIP.

Example questions or scenarios:

• "How would you optimize a matrix multiplication kernel that is memory-bound?"
• "Explain the difference between latency and throughput in the context of GPU inference."
• "How do you handle thread divergence in a GPU kernel?"

Distributed Training and Systems

For roles involving LLMs and large-scale training, this area is mandatory.

• Parallelism Strategies: Deep understanding of Data, Tensor, Pipeline, and Expert Parallelism.
• Communication: Collective primitives (All-Reduce, All-Gather, Reduce-Scatter) and their impact on training speed.
• Frameworks: Internals of Megatron-LM, DeepSpeed, FSDP (Fully Sharded Data Parallel), or Ray.
• Advanced concepts: ZeRO-Offload, gradient checkpointing, and mixed-precision training (FP16/BF16/FP8).

Example questions or scenarios:

• "Design a training cluster for a 175B parameter model. How do you split the model across GPUs?"
• "What happens during the backward pass in a distributed training setup?"
• "How does Ring All-Reduce work, and what is its bandwidth requirement?"

ML Framework Internals (PyTorch/JAX)

AMD engineers often work below the Python API.

• Computation Graphs: Eager execution vs. Graph mode (tracing/compilation).
• Compilers: Understanding XLA, TorchInductor, or TVM.
• Custom Ops: How to register a C++ operator in PyTorch.

Example questions or scenarios:

• "Describe the lifecycle of a tensor in PyTorch from creation to execution on the device."
• "How does automatic differentiation work implementation-wise?"

Coding and Algorithms

Expect standard coding questions, but often with constraints relevant to systems programming.

• Data Structures: Trees, Graphs, Linked Lists, Hash Maps.
• Algorithms: BFS/DFS, Dynamic Programming, Sorting.
• C++ Specifics: Smart pointers, references vs. pointers, virtual functions, and STL containers.

Example questions or scenarios:

• "Implement a thread-safe LRU cache."
• "Given a stream of data, find the median efficiently."
• "Detect a cycle in a directed graph."

5. Key Responsibilities

As a Machine Learning Engineer at AMD, your daily work is centered on ensuring that the AMD hardware ecosystem is a first-class citizen for AI workloads. You will spend significant time profiling end-to-end training pipelines to identify why a specific model (like Llama-3 or Stable Diffusion) might be underutilizing the GPU. This involves using profilers to look at kernel execution traces and then modifying the model code or the underlying framework (PyTorch/JAX) to improve efficiency.

Collaboration is a major component of the role. You will work closely with hardware architects to provide feedback on future GPU designs based on software bottlenecks you encounter today. You will also collaborate with the open-source community, pushing upstream changes to projects like PyTorch, Hugging Face, or vLLM to ensure they support ROCm out of the box.

For infrastructure-focused roles, you will design and build the orchestration layers that manage thousands of GPUs. This includes configuring Kubernetes clusters, optimizing job schedulers like Slurm or Ray, and automating the validation pipelines that ensure new driver updates don't regress model convergence. You are essentially building the factory that builds the models.

6. Role Requirements & Qualifications

AMD seeks engineers who are technically versatile and unafraid of low-level complexity.

Must-have skills

• Strong Programming: Expert proficiency in Python and C++. You must be able to write performance-critical code.
• ML Frameworks: Deep experience with PyTorch, JAX, or TensorFlow, including distributed training APIs.
• GPU Fundamentals: Solid understanding of GPU architecture (Nvidia CUDA or AMD ROCm/HIP) and parallel computing concepts.
• Distributed Systems: Experience with multi-node training, NCCL/RCCL, and frameworks like Megatron-LM or DeepSpeed.

Nice-to-have skills

• ROCm/HIP Experience: Prior experience specifically with AMD's software stack is a massive plus but not always required if you have strong CUDA skills.
• Compiler Knowledge: Familiarity with ML compilers like MLIR, XLA, or Triton.
• Kernel Authoring: Ability to write custom GPU kernels.
• LLM Specifics: Experience with quantization (AWQ, GPTQ), KV-caching, or vLLM internals.

7. Common Interview Questions

The following questions are representative of what you might face. They focus heavily on systems, architecture, and optimization rather than just model theory.

High-Performance Computing & Architecture

• What is the difference between a wave/warp and a thread block? How does this affect occupancy?
• Explain how you would debug a "silent data corruption" error in a GPU kernel.
• How do you optimize a kernel that is suffering from bank conflicts in shared memory?
• Compare the memory consistency models of CPU and GPU.
• What is the roofline model, and how do you use it to analyze performance?

Distributed Machine Learning

• Explain the trade-offs between Data Parallelism and Model Parallelism. When would you use one over the other?
• How does ZeRO-3 optimization reduce memory usage compared to standard Data Parallelism?
• In a multi-node cluster, how do you diagnose if the network is the bottleneck?
• Describe the communication pattern of a Transformer layer during distributed training.
• How would you implement gradient accumulation manually?

Coding & Systems Design

• (C++) Implement a custom memory allocator.
• (Python) How does the Global Interpreter Lock (GIL) impact multi-threaded ML data loading?
• Design a system to serve a Large Language Model with low latency to thousands of concurrent users.
• Write a function to perform matrix multiplication and optimize it for cache locality.
• Given a dependency graph of tensor operations, find the optimal execution order.

8. Frequently Asked Questions

Q: Do I need to know AMD ROCm beforehand, or is CUDA experience enough? While ROCm experience is a huge plus, AMD frequently hires engineers with strong CUDA backgrounds. The key is demonstrating that you understand the concepts of GPU computing (threads, blocks, memory hierarchy) which transfer well between CUDA and HIP. Show a willingness to learn the AMD stack.

Q: How much coding is involved in the interview vs. system design? Expect a balance. Junior to Mid-level roles will have heavier coding (LeetCode + C++ concepts). Senior and Staff roles will pivot more toward system design, architectural trade-offs, and deep dives into your past projects, though you will still likely face a coding round to verify hands-on skills.

Q: What is the culture like within the AI teams at AMD? The culture is often described as engineering-centric and collaborative. Because AMD is competing against a dominant market leader, there is a strong sense of shared mission and "scrappiness." Teams are often smaller and flatter than at larger competitors, giving you more ownership and visibility.

Q: Is this role remote or onsite? Most job descriptions for these roles specify a Hybrid schedule, typically requiring you to be in the office (San Jose, Bellevue, Santa Clara, or Austin) a few days a week. This is often necessary for access to hardware labs and close collaboration with silicon teams.

Q: How does the interview difficulty compare to other Big Tech companies? The difficulty is comparable to other top-tier hardware and AI infrastructure companies. The "bar" for low-level systems knowledge (C++, memory management, architecture) is often higher than at companies that focus purely on consumer web applications.

9. Other General Tips

Know the MI300 Before your interview, read the whitepapers and public specs for AMD’s Instinct MI300 accelerators. Understanding the architecture (e.g., its unified memory architecture or chiplet design) allows you to ask insightful questions and propose optimizations that are specific to their hardware.

Be Honest About Performance When discussing past projects, focus on metrics. Don't just say "I optimized the model." Say "I improved throughput by 30% by fusing the attention kernels and reducing global memory round-trips." AMD engineers live and breathe benchmarks; speak their language.

Brush Up on C++ Even if you apply for a Python-heavy ML role, AMD often tests C++ because their core libraries rely on it. Be ready to explain virtual destructors, move semantics, and how std::vector works under the hood.

Show Passion for the Ecosystem Frame your interest in terms of wanting to build an open alternative for the AI community. AMD prides itself on open-sourcing its stack. Expressing enthusiasm for contributing to an open ecosystem resonates well with hiring managers.

10. Summary & Next Steps

Becoming a Machine Learning Engineer at Advanced Micro Devices is an opportunity to stand at the intersection of massive-scale software and cutting-edge silicon. You will be challenged to solve hard engineering problems—optimizing kernels, scaling training to thousands of GPUs, and architecting the infrastructure that powers the next generation of AI. This role is perfect for engineers who are not satisfied with just using high-level APIs but want to understand and optimize what happens "under the hood."

To prepare, focus heavily on distributed systems, GPU architecture, and C++/Python proficiency. Go beyond the basics of model training and dive deep into how frameworks execute graphs and manage memory. Review the specifics of the ROCm ecosystem and be ready to discuss how you measure and improve performance.

The compensation data above reflects the competitive nature of these specialized roles. At the Principal and Staff levels, equity (RSUs) becomes a significant component of the total package, rewarding you for the long-term success and growth of AMD's AI business.

You have the potential to make a tangible impact on the AI landscape by enabling an open, high-performance alternative for the world’s developers. Approach your preparation with curiosity and rigor, and you will be well-positioned to succeed. For more practice questions and deep-dive guides, continue exploring Dataford. Good luck!