What is a Research Scientist?

As a Research Scientist at AMD, you drive the breakthroughs that power next‑generation compute—from foundation models (LLMs/LMMs) and neural rendering to the GPU kernels and software stacks that make them fast, efficient, and scalable. Your work connects fundamental research to silicon, compilers, drivers, and frameworks, ensuring ideas translate into measurable impact on AMD’s Instinct accelerators, Radeon graphics, and the broader ROCm AI ecosystem.

You will collaborate with world-class researchers and engineers on pretraining, finetuning, reinforcement learning, and inference acceleration for cutting-edge models. Expect to influence architecture choices, guide platform roadmaps, and publish at top venues—while also delivering production-grade prototypes for gaming, data center, and developer education initiatives (e.g., AMD’s advanced graphics programs and AI enablement teams).

This role is both critical and energizing: you are the force multiplier that pushes state-of-the-art AI forward while making it run faster and more efficiently on AMD hardware. Whether you’re advancing speculative decoding, optimizing attention kernels for MI-series GPUs, or translating new neural rendering techniques into real-time engines, your research moves from paper to product—and into the hands of millions of users.

Getting Ready for Your Interviews

AMD interviews favor depth, clarity, and end-to-end thinking. You’ll be assessed not only on your technical mastery, but also on your ability to design scalable systems, communicate complex ideas crisply, and collaborate across hardware, software, and product teams. Focus your preparation on fundamentals you can defend with data, and on experiences that show you can translate research into measurable platform or product wins.

• Role-related Knowledge (Technical/Domain Skills) - Interviewers look for command of your research area: transformers and training at scale, GPU architecture and kernels, neural rendering pipelines, or model optimization for inference. Demonstrate fluency with frameworks (PyTorch/JAX/TF), distributed training, transformer internals, and AMD’s ROCm stack. Show you’ve reproduced SOTA results and improved on them in real workloads.

• Problem-Solving Ability (How you approach challenges) - You will face open-ended, systems-level problems with hardware/software constraints. Interviewers evaluate how you break down ambiguity, build hypotheses, measure rigorously, and iterate. Use structured reasoning, quantify trade-offs, and reference experiments, profiling data, and ablations.

• Leadership (How you influence and mobilize others) - AMD values principled leaders who elevate teams through technical direction, mentorship, and cross-functional influence. Show you can drive proposals through research, platform, and product stakeholders; highlight where you unblocked teams, created shared artifacts (design docs, education content), and landed decisions.

• Culture Fit (How you work with teams and navigate ambiguity) - Expect questions about collaboration, intellectual humility, and execution under evolving requirements. Strong candidates show curiosity, a bias for clarity and impact, and a willingness to ship iteratively while maintaining scientific rigor.

Interview Process Overview

You will experience a rigorous, research-forward process balanced with practical engineering assessments. AMD’s interviews emphasize how you think—your experimental discipline, your grasp of performance bottlenecks, and your ability to turn research into real systems. The tone is professional, direct, and collaborative; expect probing follow-ups that push you to quantify claims and defend methodologies.

Most candidates encounter a blend of research deep dives, hands-on problem solving, and cross-functional conversations (e.g., with compiler, kernel, or graphics teams). The pace is focused but fair: you’ll typically present prior work, then engage in technical discussions around scaling, optimization, and deployment—often grounded in AMD’s GPU stack and the realities of production constraints. Strong communication and a data-backed approach are crucial.

AMD’s philosophy centers on excellence without theatrics: fewer puzzles, more substance. Expect interviews tailored to your specialization—LLMs/LMMs, generative AI for graphics, or systems/education enablement—and designed to evaluate both originality and engineering execution.

This visual details the typical sequence—from recruiter screens to technical deep dives, research talks, and cross-functional sessions—so you can align your preparation to each phase. Use it to plan when to emphasize your publications, when to showcase systems design, and when to highlight kernel or framework expertise. Build in time to rehearse your talk and assemble artifacts (profiling traces, ablations, benchmark results) well before the technical rounds.

Deep Dive into Evaluation Areas

Foundation Models and Training at Scale

This is central for roles focused on LLMs/LMMs and GenAI. Interviewers assess how you pretrain, finetune, align, and evaluate large models—and how you reason about data, optimization, and performance at scale on AMD hardware.

Be ready to go over:

• Transformer internals: attention variants, positional encodings, parameterization, normalization, activation checkpointing
• Training at scale: data/model pipeline design, mixed precision (BF16/FP8), ZeRO/FSDP/tensor/pipeline parallelism, sharding strategies
• Post-training/Alignment: SFT, RLHF/RLAIF, DPO, reward modeling, evaluation and safety considerations
• Advanced concepts (less common): speculative decoding, MoE routing/placement, KV cache policies, long-context methods, curriculum/data deduplication

Example questions or scenarios:

• "Design a training plan for a 70B model on MI-series GPUs. How do you partition, schedule, and profile it on ROCm?"
• "Walk through an RLHF pipeline end-to-end. What failure modes do you anticipate and how will you measure improvement?"
• "You claim a 12% speedup from fused attention—show how you validated correctness and reproducibility."

GPU Architecture, Kernels, and Performance Engineering

For neural rendering and inference-acceleration roles, expect deep dives into GPU architecture, kernel design, and shader optimization. You’ll be asked to connect the math of ML operators to low-level performance.

Be ready to go over:

• GPU fundamentals: compute units, memory hierarchy, occupancy, wavefronts/warps, vectorization
• Kernel development: HIP/CUDA/HLSL, fused ops, tensor core usage, shared memory tiling, synchronization
• Inference optimization: ONNX deployment, quantization, KV cache optimization, graph capture
• Advanced concepts (less common): block-sparse attention, low-level ISA, asynchronous execution, compiler passes and scheduling

Example questions or scenarios:

• "Optimize a tiled matmul with shared memory. How do you tune block sizes and handle bank conflicts?"
• "Translate a PyTorch attention op to an efficient GPU shader. What metrics and tools do you use to profile on ROCm?"
• "Diagnose a perf regression in a fused kernel after a compiler update."

Research Rigor, Experimentation, and Publication

AMD values scientists who pair bold ideas with disciplined science. You’ll be assessed on how you form hypotheses, structure experiments, and communicate outcomes—often leading to top-tier publications and high-quality internal artifacts.

Be ready to go over:

• Experimental design: ablations, baselines, statistical validity, reproducibility, error bars
• Benchmarking: dataset curation, eval metrics, fairness/robustness, regression tracking
• Publishing: framing contributions, related work, open-source artifacts for community impact
• Advanced concepts (less common): scaling laws, data filtering/tokenization pipelines, synthetic data

Example questions or scenarios:

• "Your pretraining curve plateaus—what hypotheses do you test first, and how?"
• "Design an evaluation suite for vision-language tasks that resists overfitting to benchmarks."
• "How did you craft your NeurIPS paper’s ablations to isolate causal improvements?"

Systems Design for AI: Data, Training, and Inference

You’ll design end-to-end systems that meet latency, throughput, and cost targets. Interviewers look for principled trade-offs across data pipelines, training orchestration, and deployment on AMD accelerators.

Be ready to go over:

• Data pipelines: sharding, caching, streaming, deduplication, tokenization at scale
• Training orchestration: cluster setup, checkpointing, fault tolerance, monitoring
• Inference systems: batching, dynamic sequence lengths, scheduling, memory-bound vs compute-bound analysis
• Advanced concepts (less common): speculative decoding pipelines, mixture-of-experts routing at inference, multi-tenant scheduling

Example questions or scenarios:

• "Design a low-latency inference service for a 13B chat model with spiky traffic."
• "Select and justify a distributed strategy for a 175B pretrain on ROCm."
• "Trade-offs of FP8 vs BF16 for a long-context inference workload."

Communication, Leadership, and Developer Education

Some AMD teams (e.g., AI developer enablement) emphasize translating complex research into compelling content and prototypes. Interviewers evaluate your clarity, influence, and ability to scale knowledge across the ecosystem.

Be ready to go over:

• Technical storytelling: teach advanced topics simply and accurately
• Artifacts: design docs, tutorials, conference talks, internal trainings
• Cross-functional leadership: aligning research, platform, and product timelines
• Advanced concepts (less common): content strategy for global developer audiences, measuring learning outcomes

Example questions or scenarios:

• "Explain speculative decoding to senior leadership and to a new grad—how do the explanations differ?"
• "Outline a tutorial for KV-cache optimizations on ROCm that leads to hands-on success."
• "Describe a time you built consensus across research and compiler teams."

Graphics and Neural Rendering (Role-Dependent)

For Advanced Graphics Program roles, expect a fusion of ML and real-time rendering. You’ll be tested on the theory and practice of integrating neural methods into production pipelines.

Be ready to go over:

• Neural operators in graphics: inverse rendering, denoisers, super-resolution, radiance fields
• Graphics APIs: DirectX/Vulkan/OpenGL, shader integration, engine constraints
• Performance: latency budgets, VR/AR constraints, perceptual quality metrics
• Advanced concepts (less common): diffusion for texture/material synthesis, neural scene representations, pipeline toolchains

Example questions or scenarios:

• "Integrate a neural denoiser into a DX12-based render loop; identify bottlenecks."
• "Compare diffusion vs transformer-based upscalers for real-time constraints."
• "Design datasets and metrics to evaluate neural rendering artifacts."

Use this visualization to map the interview emphasis areas: expect clusters around Transformers/LLMs, distributed training, ROCm/GPU kernels, quantization/KV cache, and for graphics roles, neural rendering and shader optimization. Prioritize your study plan by doubling down on the largest terms that match your targeted track and filling any gaps in the smaller, specialized topics.

Key Responsibilities

In this role, you transform cutting-edge research into performant, reliable systems on AMD hardware. Day-to-day work balances ideation, prototyping, optimization, and cross-functional alignment.

• You will design and train large models, run finetuning/RLHF, and develop inference acceleration techniques that materially improve latency and throughput.
• You will profile and optimize ML operators, write or review GPU kernels/shaders (HIP/CUDA/HLSL), and collaborate closely with compiler, driver, and hardware teams to land end-to-end gains.
• You will author technical docs, publish at top-tier venues, and, in some tracks, build developer education content (tutorials, talks, sample repos) to scale impact across the ecosystem.

Expect to contribute to:

• Pretraining and post-training pipelines with robust data curation, evaluation suites, and monitoring
• Kernel and graph-level optimizations for attention, matmul, and fused ops on ROCm
• Neural rendering prototypes integrated into modern engines with strict frame budgets
• Internal IP and external publications, including open-source contributions where appropriate

Role Requirements & Qualifications

Successful candidates combine deep domain expertise with systems-level thinking and strong execution.

• Must-have technical skills

  • Python and deep learning frameworks (PyTorch preferred; JAX/TF a plus)
  • Transformers/LLMs/LMMs, including training, finetuning, and alignment methods
  • Distributed training (FSDP/ZeRO, tensor/pipeline parallelism), mixed precision (BF16/FP8)
  • Performance profiling and optimization for training/inference on GPUs; familiarity with ROCm
  • For graphics tracks: C++, HIP/CUDA/HLSL, shader optimization, and modern graphics APIs

• Experience expectations

  • Advanced degree (MS/PhD) in ML/CS/EE or equivalent practical research experience
  • Demonstrated SOTA reproduction and improvement; strong results in large-scale settings
  • For senior/staff: cross-functional leadership, technical direction, and shipped prototypes

• Soft skills that set you apart

  • Clear, concise communication tailored to engineers, researchers, and leadership
  • Evidence-driven decision making; comfort with ambiguous, evolving problem spaces
  • Ability to mentor, document, and scale knowledge across teams and developers

• Nice-to-haves

  • Publications at top venues (NeurIPS/ICLR/ICML/CVPR/SIGGRAPH), open-source contributions
  • Low-level knowledge (ISA/PTX/GPU memory models), quantization, compression
  • Experience building developer education content or public talks

This snapshot aggregates compensation data from recent AMD postings for research-focused roles, with variation by level (Senior/Staff), specialization (LLMs vs. neural rendering), and location (e.g., Santa Clara, Bellevue). Treat it as directional; total compensation typically includes base salary, bonus, and equity—specifics are confirmed during your offer process.

Common Interview Questions

Expect a blend of deep technical, systems design, and research communication questions. Prepare concise, high-signal answers supported by data, diagrams, or brief pseudocode where useful.

Technical and Domain Knowledge

Interviewers will probe your mastery of models, operators, and frameworks.

• Explain the trade-offs among attention mechanisms (full, linear, block‑sparse) for long contexts.
• How do ZeRO stages compare to FSDP for a 70B model on MI-series GPUs?
• Walk through implementing FP8 training: scaling, calibration, and stability pitfalls.
• Contrast RLHF, DPO, and RLAIF for instruction tuning—when would you favor each?
• How would you reproduce and then improve a SOTA vision-language benchmark?

System Design and Architecture

You’ll design robust training and inference systems within hardware constraints.

• Design a scalable pretraining pipeline (data ingestion to checkpointing) for 1T tokens/month.
• Architect a low-latency inference service for a 13B chat model with bursty traffic on ROCm.
• Propose a KV‑cache management strategy for multi-tenant serving on limited HBM.
• Plan a migration from CUDA to HIP for a fused attention op—risks and validation steps.
• Outline observability for training at scale: what to log, how often, and why.

Research Rigor and Experimentation

Expect to defend your methodology and results.

• Your model underperforms on out-of-domain evals—diagnosis plan and ablations?
• Describe an experiment you designed that invalidated your initial hypothesis.
• How do you ensure reproducibility under nondeterminism and distributed noise?
• Show how you separate data quality effects from model architecture gains.
• What constitutes a publishable contribution vs. an engineering optimization?

GPU Kernels, Inference, and Graphics (Role-Dependent)

Low-level performance and engine integration for neural rendering and inference.

• Optimize a tiled matmul kernel: occupancy, tiling, and memory coalescing choices.
• Convert a PyTorch op to an efficient GPU shader—walk through profiling and tuning.
• Evaluate diffusion vs. transformer upscalers for real-time rendering constraints.
• Quantize an LLM to 4-bit: accuracy retention strategies and runtime trade-offs.
• Diagnose a 15% perf drop after a compiler update—where do you look first?

Behavioral and Leadership

Demonstrate collaboration, clarity, and ownership.

• Tell us about a time you aligned research, compiler, and product stakeholders.
• Describe a difficult trade-off you made to ship on time—what did you cut and why?
• How do you mentor junior researchers while keeping velocity?
• Share an example of communicating complex research to non-experts.
• When did you change your mind based on new data? What was the outcome?

Coding and Practical Exercises

Expect pragmatic coding or pseudocode; correctness and clarity matter.

• Implement beam search with length penalties and discuss performance optimizations.
• Write pseudocode for block‑sparse attention; explain indexing and memory layout.
• Parse and stream a large dataset with fault tolerance; outline retry semantics.
• Implement gradient checkpointing in a transformer block; analyze memory savings.
• Sketch a microbenchmark to isolate kernel vs. memory bottlenecks.

Use this interactive practice to rehearse answers and pressure-test your reasoning on Dataford. Prioritize categories most relevant to your target track and simulate time-bound responses to build clarity and confidence.

Frequently Asked Questions

Q: How difficult are AMD research interviews, and how long should I prepare?
Interviews are rigorous and evidence-driven. Most candidates benefit from 3–6 weeks of focused prep across domain depth (LLMs/neural rendering), systems design, and GPU performance fundamentals.

Q: What differentiates successful candidates?
Strong candidates present clear, reproducible results, quantify trade-offs, and connect research to measurable platform or product impact. They communicate crisply, show humility under challenge, and demonstrate cross-functional influence.

Q: What is the culture like in research teams?
Teams are collaborative, direct, and impact-oriented. You’ll find a strong bias for clarity, open problem-solving, and close coordination with hardware, compiler, and software groups.

Q: How fast is the interview process?
Timelines vary by role and location, but once interviews begin, expect a focused multi-week process. Keep your availability clear, and proactively share artifacts (papers, repos, perf reports) to streamline evaluation.

Q: Is hybrid or remote work available?
Many roles are hybrid in Santa Clara, San Jose, or Bellevue; select US-remote options may be available depending on team needs and level. Confirm details with your recruiter.

Q: Will I need to give a research talk?
Often yes. Plan a 30–45 minute talk highlighting 1–2 impactful projects, with ablations, profiling data, and clear contributions. Prepare a shorter 5–7 minute version for panel intros.

Other General Tips

• Anchor claims with data: Bring charts, ablations, and perf traces; be ready to discuss methodology, error bars, and variance.
• Think end-to-end: Tie model choices to hardware realities (HBM, bandwidth, occupancy) and product SLAs (latency, cost).
• Know ROCm basics: API surface, kernel launch semantics, tooling; if you know CUDA well, prepare to contrast and translate.
• Show reproducibility discipline: Seeds, checkpoints, logging, and config management—this is a credibility marker.
• Prepare dual narratives: One for deep experts and one for non-experts; interviewers will test your audience agility.
• Have a point of view: On FP8 viability, KV-cache strategies, speculative decoding, or MoE routing—defend with evidence.

Summary & Next Steps

The Research Scientist role at AMD sits at the intersection of bold ideas and high-performance execution. You will shape the future of AI—pushing LLMs/LMMs, neural rendering, and GPU-accelerated systems to new heights—while making them practical, efficient, and widely usable on AMD platforms.

Concentrate your preparation on five fronts: transformer fundamentals and training at scale, GPU architecture and kernel performance, systems design for training/inference, research rigor and publication quality, and clear, influential communication. Build a compact portfolio of artifacts—slides, benchmark reports, and code snippets—that make your impact legible and defensible.

Use this guide to structure your plan, then deepen your practice with the interactive question bank on Dataford. You are stepping into a role where your work can change how the world computes—prepare with purpose, lead with clarity, and show the measurable results that only you can deliver. Together, we advance.