Applied Scientist

Adobe Adobe · Enterprise · San Jose, CA

Applied Scientist at Adobe focused on transforming research breakthroughs in Generative AI, LLMs, and multimodal AI into innovative product features for millions of users. The role involves scouting, adapting, and improvising the latest research, prototyping, and deploying practical innovations, with a strong emphasis on training and optimizing large-scale models, including post-training techniques and GPU-accelerated pipelines for both training and inference.

What you'd actually do

  1. Transform innovative research concepts into practical applications within the realm of Generative AI, LLMs, Reinforcement learning, Reasoning, Evaluations.
  2. Prototype and experiment rapidly, demonstrating feasibility and business impact.
  3. Push beyond academic results to develop and deploy practical, differentiated innovations for Adobe’s products.
  4. Collaborate with world-class researchers and ML engineers to bring research ideas to production.
  5. Develop and enhance GPU-accelerated pipelines for (customized) model training and inference, focusing on performance, scalability, and reliability.

Skills

Required

  • Ph.D. or Masters or equivalent experience in Engineering, Computer Science, AI/ML or related fields and 10+ professional experience.
  • Research or industry experience in training AI/ML models in at least one of the following modalities: multimodal LLMs, Image, Video.
  • Proficiency in training and optimizing large-scale models, involving data curation, distributed training, and memory-efficient strategies.
  • Experience with post-training techniques such as fine-tuning, alignment or distillation.
  • Proficiency with modern deep learning frameworks (e.g., PyTorch) and experience scaling models on GPU/TPU clusters.

Nice to have

  • Familiarity with inference optimization, performance trade-offs, and scalable integration will be an asset.
  • Experience on large-scale generative model training.
  • Experience on synthetic data generation.
  • Previous involvement with product teams in technology transfers.
  • Experience of working with large-scale datasets.
  • 4-7 years of experience in relevant fields.

What the JD emphasized

  • training AI/ML models
  • training and optimizing large-scale models
  • model training and inference

Other signals

  • Generative AI
  • LLMs
  • multimodal AI
  • image/video generation/editing
  • research to product