Senior Staff Applied Scientist - Ai/ml

Adobe Adobe · Enterprise · San Jose, CA

Senior Staff Applied Scientist role at Adobe focusing on transforming AI/ML research breakthroughs into innovative product features for Generative AI, LLMs, and multimodal AI. The role involves scouting, adapting, and improvising research, prototyping, rapid experimentation, and deploying practical innovations for Adobe's products. Responsibilities include developing and enhancing GPU-accelerated pipelines for model training and inference, collaborating with researchers and ML engineers, and publishing work. Requires a Ph.D. or Masters with 10+ years of experience in AI/ML, with expertise in training AI/ML models in multimodal LLMs, Image, or Video, proficiency in training and optimizing large-scale models, and experience with post-training techniques like fine-tuning and alignment.

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. Publish and present your work in world-class scientific venues in AI/ML fields.

Skills

Required

  • Ph.D. or Masters or equivalent experience in Engineering, Computer Science, AI/ML or related fields
  • 10+ years of professional experience
  • Training AI/ML models in multimodal LLMs, Image, or Video
  • Training and optimizing large-scale models
  • Data curation
  • Distributed training
  • Memory-efficient strategies
  • Post-training techniques (fine-tuning, alignment, distillation)
  • Modern deep learning frameworks (e.g., PyTorch)
  • Scaling models on GPU/TPU clusters
  • Excellent communication skills
  • Teamwork

Nice to have

  • Familiarity with inference optimization
  • Familiarity with performance trade-offs
  • Familiarity with scalable integration
  • Large-scale generative model training
  • Synthetic data generation
  • Technology transfers with product teams
  • Working with large-scale datasets

What the JD emphasized

  • 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

Other signals

  • transform research into product features
  • advance AI capabilities
  • impact millions of users
  • develop and deploy practical, differentiated innovations
  • bring research ideas to production
  • GPU-accelerated pipelines for model training and inference