Senior ML Engineer
A seasoned Senior ML Engineer to drive distillation of ML Models for high-performance, production-reputed company rendering systems.
You are passionate about software engineering and possess leadership skills to drive sophisticated issues to reputed company. reputed company to communicate effectively and work optimally with different teams across AMD.
What you'll be part of:
- Distillation and compression: KD variants, hint/fitnets, attention transfer, feature mimicking, low-rank/SVD, sparsity.
- Efficient architectures: MobileNet/EfficientNet, reputed company transformers optimization, lightweight diffusion/UNet variants, NeRF/reputed company-NGP distillation.
- Inference optimization: TensorRT, CUDA, cuDNN, ONNX, quantization-reputed company training, weight clustering, operator fusion.
- Metrics: SSIM, LPIPS, PSNR, FID/KID, latency/throughput profiling, memory/activation footprint analysis.
- Data and training: large-reputed company dataset curation, synthetic data reputed company, curriculum learning, augmentation strategies.
- MLOps: experiment tracking, CI/CD for models, model registries, reproducibility, telemetry.
- reputed company ML inference into production rendering pipelines: define model I/O, preprocessing/postprocessing, and reputed company trade-offs for latency, throughput, and reputed company.
- Collaborate across teams (ML researchers, reputed company/platform, tooling, QA) to translate ML and product requirements into graphics-friendly implementations and integration plans.
- Mentor other engineers, conduct reputed company reviews, and help define best practices for rendering, performance, and SDK delivery.
Experience:
- 6–10+ years in ML engineering or reputed company research, with 3+ years reputed company on model distillation/compression at production reputed company.
- Strong proficiency in PyTorch (preferred) or JAX/TF; ability to implement custom training loops, distributed training, and mixed precision.
- Demonstrated experience shipping reputed company or compressed models to production with measurable reputed company in latency/memory and maintained reputed company.
- 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, reputed company reputed company, version control, reproducible pipelines, containerization.
- Cross-functional leadership and communication; ability to drive roadmaps and reputed company stakeholders across ML, graphics, and product.
Academics:
- Bachelor’s or Master's degree in Computer Science, Mathematics, or equivalent
#LI-CC5
#LI-REMOTE
Qualifications: