Candidate Profile — Candidate #2
Position: Adobe — Machine Learning Infrastructure Engineer (San Jose, CA; San Francisco, CA; Seattle, WA)
Candidate Location: San Francisco Bay Area
Experience: 6.8 years experience
Relevant Experience & Education Highlights
Machine learning engineer with deep PyTorch and TensorFlow expertise, scalable pipeline development, and cloud deployment experience, strongly aligning with building Adobe's large-scale generative AI infrastructures for Firefly.
1. You'll build and optimize infrastructures that power large foundation model training on thousands of GPUs
Engineered high-performance ML systems and model ensembles demonstrating optimization for compute-intensive workloads.
- Engineered cutting-edge object detection pipeline achieving 100% accuracy in anomaly detection while expanding image generator module at a leading semiconductor company.
- Pioneered ensemble method integrating AlexNet, ResNet, VGG, and custom CNNs, boosting anomaly detection accuracy to 99% and reducing false positives without additional training.
- Developed LLM models, computer vision solutions, and Graph Neural Networks using PyTorch and TensorFlow for advanced AI applications.
2. Architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust
Led development of data pipelines, ETL processes, and microservices for production-scale ML operations.
- Orchestrated end-to-end microservices development, increasing marketing campaign click-through rates by 50% at a digital marketing firm.
- Developed complex queries and large-volume data pipelines for audience selection strategies serving a Fortune 25 retail giant.
- Leveraged ETL processes for data integration, powering statistical models, boosting sales by 12%, and enabling NLP-driven customer segmentation.
- Dockerized full-stack applications with Flask, Vue.js, and NLP preprocessing, consolidating log sources and reducing debugging time by 30%.
3. Strong Python and deep learning engineering skills, paired with experience in training and inferencing with PyTorch or TensorFlow
Applied Python, PyTorch, and TensorFlow across diverse ML projects including optimization and deployment.
- Utilized PyTorch and TensorFlow for training and inference in anomaly detection, computer vision, and LLM model development.
- Designed algorithms improving model accuracy by 32 times for distribution center-to-store inventory alignment.
- Orchestrated ML pipelines with PCA, Gradient Boosting Regression Trees, SVM, and SVR for IoT-based real-time data analysis in smart irrigation systems.
Requirements & Candidate Alignment
| Adobe Requirement | Candidate Qualification |
|---|---|
| Graduate, PhD, or postgraduate degree in Computer Science, Computer Engineering, or a related field—or equivalent experience. | Master's degree in Data Science from a state university, complemented by Bachelor's in Computer Science and Engineering. |
| 2+ Years ML Engineering experience, specializing in generative AI like LLMs. | 6.8 years ML engineering experience across roles in LLMs, computer vision, anomaly detection, and deep learning pipelines. |
| Strong Python and deep learning engineering skills, paired with experience in training and inferencing with PyTorch or TensorFlow. | Proficient in Python, PyTorch, and TensorFlow for model training, inference, and optimization including CNN ensembles. |
| Familiarity with distillation, transformers, and diffusion models. Experience with generative image and video is a plus. | Experienced with transformers through LLM work and generative image capabilities via expanded anomaly detection pipelines. |
| Knowledge of deployment technologies such as Docker, ML Ops, and ML services | Skilled in Docker for containerizing full-stack ML applications and MLOps practices in production environments. |
| Experience with cloud platforms like Azure and AWS is a plus. | Hands-on with Azure, AWS, and GCP for ML model development, data pipelines, and deployments. |
| Excellent problem-solving abilities and your capacity to analyze complex issues and drive solutions with a data-driven approach. | Proven through engineering 100% accuracy pipelines, 99% ensemble models, and 32x accuracy improvements via data-driven algorithms. |
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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