Candidate Profile — Candidate #7
Position: Adobe — Machine Learning Infrastructure Engineer
Candidate Location: Morgan Hill, CA
Experience: 9.6 years experience
Relevant Experience & Education Highlights
Demonstrates deep expertise in GPU-accelerated ML infrastructures, PyTorch and TensorFlow training/inference, CUDA kernels on Nvidia A100/H100 GPUs, and transformers for LLMs, strongly aligning with Adobe's needs for scaling Firefly foundation models.
1. You'll build and optimize infrastructures that power large foundation model training on thousands of GPUs
Scaled deep learning workloads across multi-GPU clusters with custom optimizations.
- Accelerated CNN inference: Deployed TensorRT, ONNX, and custom CUDA kernels on A100 and H100 GPUs for nanopore sequencing analysis at a medical diagnostics company.
- Developed multi-GPU ML algorithms: Engineered natural language processing with transformers and BERT models in multi-node settings at a major semiconductor company.
- Optimized neural network acceleration: Implemented GPU enhancements for post-primary analysis algorithms using Nvidia ecosystem at a medical diagnostics company.
2. You will profile GPU utilization, trace inference and training runs and help craft strategies for optimizing our ML model latency
Applied advanced profiling and inference tools to enhance ML performance.
- Profiled GPU workloads: Utilized TensorRT, ONNX, Trtexec, and Nsys for inference optimization at a medical diagnostics company.
- Leveraged vLLM for LLMs: Integrated vLLM alongside PyTorch and TensorFlow for large language model inference.
- Coded GPGPU algorithms: Developed video signal processing in C and OpenCL on Nvidia SDK and Intel CodeBuilder environments.
3. We'll work together to architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust
Architected complete ML and computer vision systems for production deployment.
- Architected vehicle inspection system: Designed image processing algorithms and operational requirements for automated defect detection at an automotive inspection technology company.
- Built computer vision pipelines: Created techniques for plant mass measurement and tracking in greenhouses at an agricultural technology company.
- Engineered texture synthesis: Developed unsupervised ML-based intra-picture coding for video codecs at a leading consumer electronics company.
- Integrated deep learning frameworks: Combined PyTorch, TensorFlow, and OpenCV for computer vision and signal processing applications.
Requirements & Candidate Alignment
| Adobe Requirement | Candidate Qualification |
|---|---|
| Graduate, PhD, or postgraduate degree in Computer Science, Computer Engineering, or a related field—or equivalent experience: | PhD in Electrical Engineering from a top-tier research university |
| 2+ Years ML Engineering experience, specializing in generative AI like LLMs: | 9.6 years total experience across ML engineering roles, including transformers, BERT, and vLLM for large language models |
| Strong Python and deep learning engineering skills, paired with experience in training and inferencing with PyTorch or TensorFlow: | Strong Python proficiency with PyTorch and TensorFlow for training, inference, and deep convolutional neural networks |
| Familiarity with distillation, transformers, and diffusion models. Experience with generative image and video is a plus: | Transformers expertise with BERT and NLP algorithms; generative image techniques via unsupervised ML texture synthesis for video |
| Knowledge of deployment technologies such as Docker, ML Ops, and ML services: | Docker experience; MLOps-aligned work in multi-GPU ML infrastructures and services |
| Experience with cloud platforms like Azure and AWS is a plus: | Scalable GPU cluster experience with A100/H100 Nvidia GPUs, transferable to cloud environments |
| Excellent problem-solving abilities and capacity to analyze complex issues and drive solutions with a data-driven approach: | Proven through architecting ML systems for computer vision, signal processing, and nanopore sequencing |
If you would like to discuss this candidate or other critical roles, here is a link to my calendar to schedule a call:
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