Relevant Experience & Education Highlights
Strong alignment with multimodal reasoning and generative AI expertise, particularly Vision-Language Models and diffusion models, positions this candidate to contribute effectively to Adobe's Firefly IP guardrail systems.
1. Vision-Language & Multimodal Reasoning
Advanced work with VLMs and multimodal systems directly supports semantic IP understanding and safety-aware inference.
- Developed image search engine leveraging Vision Language Models at a leading research and advisory firm.
- Built agentic recommendation systems incorporating Vision Language Models, large language models, and reinforcement learning at a major retail company.
- Applied generative AI techniques including transcript and article summarization using large language models and retrieval-augmented generation at the same research firm.
2. Research & Technical Depth
Extensive background in applied machine learning and generative AI meets requirements for large-scale models, fine-tuning, and alignment.
- Earned certifications in Generative AI with Diffusion Models, Model Parallelism for large neural networks, and Data Parallelism for multi-GPU training.
- Led fraud detection and natural language processing initiatives as senior machine learning scientist at a major financial services company over 7 years.
- Hosted weekly paper reading and knowledge sharing sessions to drive research insights in generative AI and multimodal systems.
3. Scientific & Systems Rigor
Proven ability to conduct experiments and handle production-scale ML aligns with evaluating trade-offs and mitigating failure modes.
- Utilized Google Cloud Platform, Ray, and Hadoop for scalable machine learning workflows across multiple roles.
- Contributed to search and recommendation systems emphasizing generative AI, large language models, and reinforcement learning at a major retail company.
- Demonstrated experimental skills through PhD-level research publications and Emory University research grant.
4. Collaborative Approach
Experience in cross-functional environments supports communication with diverse teams on scientific advances.
- Facilitated weekly knowledge sharing sessions on latest papers in generative AI and Vision Language Models.
- Applied machine learning across fraud detection, search, and recommendation in team-oriented production settings.
- Pursued advanced degrees while maintaining high-impact industry roles, showcasing adaptability.
Requirements & Candidate Alignment
| Adobe Requirement | Candidate Qualification |
|---|---|
| Education: PhD or MS in Computer Science, Machine Learning, AI, or related field | PhD in Information Systems and MS in Computer Science from top-tier research universities |
| Experience: 5+ years of experience in applied ML or generative AI research (industry or academia) | 9.2 years across senior machine learning scientist, applied scientist, and research scientist roles |
| Strong background: in large-scale generative models (diffusion models, multimodal transformers, autoregressive systems) | Strong background via certifications in Generative AI with Diffusion Models and hands-on Vision Language Models for image search |
| Deep experience: with model fine-tuning, alignment strategies, and representation learning | Deep experience through reinforcement learning in agentic systems and large language model applications |
| Expertise: in Vision-Language Models or multimodal foundation models | Expertise building image search engines and recommendation systems with Vision Language Models |
| Proficiency: in Python and modern ML frameworks (e.g., PyTorch), with experience of training and deploying large models | Proficiency training large models via certifications in model and data parallelism on multiple GPUs |
| Strong experimental development and statistical evaluation skills | Strong skills from PhD research, publications, and hosting AI paper reading sessions |
| Experience working in cross-functional research-to-product environments | Extensive experience collaborating on production search, recommendation, and fraud detection systems |
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