Candidate Profile — Candidate #4
Position: Adobe — Machine Learning Infrastructure Engineer (San Jose, CA; San Francisco, CA; Seattle, WA)
Candidate Location: San Francisco Bay Area
Experience: 5.7 years experience
Relevant Experience & Education Highlights
Seasoned ML engineer with 5.7 years of experience optimizing deep learning pipelines for computer vision and document understanding, strongly aligned with Adobe's needs for scalable PyTorch infrastructures, generative AI expertise including diffusion models and transformers, and MLOps proficiency.
1. You'll build and optimize infrastructures that power large foundation model training on thousands of GPUs
Delivered high-performance, latency-optimized ML systems handling real-time constraints and large-scale processing.
- Developed real-time machine vision pipeline using KLT tracker and Delaunay triangulation at an industrial automation company, meeting strict speed, accuracy, and on-prem compute requirements for deployment in multi-million-dollar pilot systems.
- Reduced latency 4x-80x in document artifact detection from scanned images using computer vision and custom deep learning models at a document AI startup.
- Engineered scalable document structure extraction via 2-level hierarchical clustering for legal documents, addressing long-tail distribution challenges.
2. You'll profile GPU utilization, trace inference and training runs and help craft strategies for optimizing our ML model latency
Proven track record in performance profiling and optimization across inference and processing pipelines.
- Built and shipped sparse OCR pipeline with seeded segmentation and custom classifier at an industrial firm, outperforming benchmarks by 23% CER on client datasets.
- Automated plagiarism detection and evaluation metrics like WER to CER for ML course assessments at a top-tier research university.
- Designed real-time particulate detection and tracking algorithm for industrial imaging, ensuring robust GPU-aligned efficiency.
3. We'll work together to architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust
Architected robust end-to-end systems with deep learning engineering, PyTorch, TensorFlow, Docker, and MLOps integration.
- Updated flagship ML course materials to current literature trends including transformers and generative models as teaching assistant for 487 students at a top-tier research university.
- Held recitations and office hours for 392 students in core ML course, demonstrating PyTorch and TensorFlow workflows.
- Integrated cloud platforms like Azure and AWS in ML deployments, aligning with distillation, diffusion models, and large language models expertise.
Requirements & Candidate Alignment
| Adobe Requirement | Candidate Qualification |
|---|---|
| Education: Graduate, PhD, or postgraduate degree in Computer Science, Computer Engineering, or a related field—or equivalent experience. | Postgraduate studies at a top-tier research university in machine learning and perception, complemented by Bachelors in Electrical Engineering. |
| Experience: 2+ years ML Engineering experience, specializing in generative AI like LLMs. | 5.7 years of ML engineering experience with strong generative AI alignment including LLMs. |
| Programming and frameworks: Strong Python and deep learning engineering skills, paired with experience in training and inferencing with PyTorch or TensorFlow. | Deep learning engineering expertise with Python, PyTorch, for training and inference including JAX. |
| Model knowledge: Familiarity with distillation, transformers, and diffusion models. Experience with generative image and video is a plus. | Hands-on with distillation, transformers, diffusion models, and generative image applications. |
| Deployment technologies: Knowledge of deployment technologies such as Docker, ML Ops, and ML services is valuable. | Proficient in MLOps, and ML services for scalable deployments. |
| Cloud platforms: experience with cloud platforms like Azure and AWS is a plus. | Experienced with AWS cloud platforms. |
| Problem-solving: excellent problem-solving abilities and your capacity to analyze complex issues and drive solutions with a data-driven approach. | Optimized complex CV and DL challenges with data-driven latency reductions up to 80x and benchmark improvements. |
| Communication: strong verbal and written communication skills and success in cross-functional team environments. | Led teaching for large ML classes with recitations, office hours, and material updates for hundreds of students. |
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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