Candidate Profile — Candidate #1
Position: Adobe — Applied Scientist - Multimodal (San Jose, CA; San Francisco, CA; Seattle, WA)
Candidate Location: San Francisco Bay Area
Experience: 12.9 years experience
Relevant Experience & Education Highlights
Extensive leadership in generative AI, multimodal safety systems, and production-scale ML pipelines positions this candidate strongly for driving IP-aware generative modeling and alignment research at Adobe.
1. Research & Technical Depth
Demonstrates PhD-level expertise in applied ML, generative models, and multimodal systems directly aligning with role requirements.
- Designed novel foundation model architectures for image generation, including dataset optimization and scalable hybrid-parallelized training loops, at a collaborative open-source model initiative.
- Delivered custom fine-tuned diffusion models, LoRA/QLoRA training pipelines, and prompt safety systems for gaming, animation, and creative tools during independent AI consulting.
- Architected internal AI-driven production tools and IP incubation technology, integrating generative AI workflows into scalable content creation pipelines, at an AI-driven creative studio.
- Led development of PyTorch and TensorFlow-based multimodal moderation systems across image, video, audio, and text domains to ensure generative safety at a leading generative AI community platform.
2. Vision-Language & Multimodal Reasoning
Advances multimodal pipelines and safety mechanisms, supporting real-time steering and IP compliance in generative systems.
- Integrated LLMs, generative models, and multimodal pipelines into production environments for early-stage startups and creative teams.
- Implemented end-to-end multimodal moderation systems leveraging ML breadth for image, video, audio, and text safety in a generative AI ecosystem.
- Developed secure back-end systems protecting IP, production datasets, and proprietary AI tools while scaling creator marketplaces.
3. Scientific & Systems Rigor
Exhibits strong experimental skills, failure mode analysis, and production ML deployment experience for large-scale systems.
- Generated rigorous scientific publications in advanced CFD methods using cloud-based multi-physics codes and stress-tested complex physics problems at a prestigious national laboratory.
- Implemented DataBricks MLFlow and Model Registry for model pipelines, performance monitoring, and drift detection at a fintech company.
- Orchestrated multi-physics simulations across Flow3D, EXN/Aero, and SolidWorks Flow, researching surface tension effects for prototype development at a consumer health product company.
4. Collaborative Approach
Builds and leads cross-functional teams, translating research into deployed systems with diverse stakeholders.
- Built and led engineering teams, partnering with creatives, AI researchers, product, and community groups to deliver AI tooling from conception to deployment.
- Collaborated with volunteer ML scientists from academia and industry on foundation model development.
- Advised cross-functional teams on growth using supervised/unsupervised learning and large data sources.
Requirements & Candidate Alignment
| Adobe Requirement | Candidate Qualification |
|---|---|
| PhD or MS in Computer Science, Machine Learning, AI, or related field | Doctorate in Mechanical Engineering from a top-tier research university, plus Master's and Bachelor's in Mechanical Engineering from a top-tier research university |
| 5+ Years of experience in applied ML or generative AI research (industry or academia) | 12.9 years of experience across AI leadership roles in generative models, multimodal systems, and production ML |
| Strong background in large-scale generative models (diffusion models, multimodal transformers, autoregressive systems) | Deep expertise in diffusion models, LoRA/QLoRA training, foundation model architectures, and multimodal pipelines |
| Deep experience with model fine-tuning, alignment strategies, and representation learning | Delivered fine-tuned diffusion models, prompt safety systems, and IP-protecting AI tools with alignment focus |
| Expertise in Vision-Language Models or multimodal foundation models | Developed multimodal moderation across image, video, audio, text; integrated LLMs and generative pipelines |
| Proficiency in Python and modern ML frameworks (e.g., PyTorch) | Proficient in Python, PyTorch, TensorFlow for training, deploying large models, and MLOps with MLflow |
| Strong experimental development and statistical evaluation skills | Produced scientific publications, stress-tested simulations, and monitored model performance with statistical rigor |
| Experience working in cross-functional research-to-product environments | Led engineering teams collaborating with researchers, creatives, product, and founders on deployed AI systems |
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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