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AMD Logo AMD · IT - Software Development

Senior ML Engineer- Distillation

Poland, Warsaw · Job Posted April 05, 2026
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Job Description

A seasoned Senior ML Engineer who drives distillation of ML Models for high-performance, production-ready rendering systems. You are passionate about software engineering and possess leadership skills to drive sophisticated issues to resolution. Able to communicate effectively and work optimally with different teams across AMD.

Job Responsibility

  • Distillation and compression: KD variants, hint/fitnets, attention transfer, feature mimicking, low-rank/SVD, sparsity
  • Efficient architectures: MobileNet/EfficientNet, vision transformers optimization, lightweight diffusion/UNet variants, NeRF/instant-NGP distillation
  • Inference optimization: TensorRT, CUDA, cuDNN, ONNX, quantization-aware training, weight clustering, operator fusion
  • Metrics: SSIM, LPIPS, PSNR, FID/KID, latency/throughput profiling, memory/activation footprint analysis
  • Data and training: large-scale dataset curation, synthetic data generation, curriculum learning, augmentation strategies
  • MLOps: experiment tracking, CI/CD for models, model registries, reproducibility, telemetry
  • Integrate ML inference into production rendering pipelines: define model I/O, preprocessing/postprocessing, and make trade-offs for latency, throughput, and quality
  • Collaborate across teams (ML researchers, engine/platform, tooling, QA) to translate ML and product requirements into graphics-friendly implementations and integration plans
  • Mentor other engineers, conduct code reviews, and help define best practices for rendering, performance, and SDK delivery

Requirements

  • 6–10+ years in ML engineering or applied research
  • 3+ years focused on model distillation/compression at production scale
  • Strong proficiency in PyTorch (preferred) or JAX/TF
  • Ability to implement custom training loops, distributed training, and mixed precision
  • Demonstrated experience shipping distilled or compressed models to production with measurable gains in latency/memory and maintained quality
  • Deep understanding of knowledge distillation techniques: teacher–student frameworks, soft-labels, intermediate feature matching, contrastive distillation, task-specific loss shaping
  • Hands-on experience with quantization (static/dynamic, PTQ/QAT), pruning, and graph-level optimizations (operator fusion)
  • GPU performance engineering: CUDA fundamentals, TensorRT/ONNX Runtime, kernel profiling (Nsight), memory/layout optimization
  • Solid grasp of computer graphics fundamentals: rendering pipeline, shaders, sampling, anti-aliasing, tone mapping, and perceptual metrics
  • Strong software engineering: Python/C++ proficiency, testing, code quality, version control, reproducible pipelines, containerization
  • Cross-functional leadership and communication
  • ability to drive roadmaps and align stakeholders across ML, graphics, and product
  • Bachelor’s or Master's degree in Computer Science, Mathematics, or equivalent
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