Sr. Applied Scientist

Adobe Adobe · Enterprise · San Jose, CA +1

This role focuses on developing and implementing ML models for search, recommendations, and generative AI use cases within Adobe's creative suite. It involves fine-tuning LLMs and Diffusion models, optimizing inference, and processing multimodal data to assist users and recommend assets. The role also includes research into novel LLM architectures and evaluation methods.

What you'd actually do

  1. Research, develop and implement ML models using Transformers, LLMs, and classical ML algorithms for search and recommendation use cases
  2. Be proficient in Python, PyTorch, Hugging Face Ecosystem, distributed and scalable fine-tuning of state-of-the-art LLMs.
  3. Be proficient in optimized inference engines/frameworks like vLLM, TensorRT
  4. Fine-tune LLMs and Diffusion models to build and augment a creative knowledge graph and use it to recommend billions of images, videos, documents, and other assets in real time.
  5. Process Multimodal (Text, Image, Video) intent and contextually assist the user by recommending ingredients and generating rich artifacts to enable a seamless creative path.

Skills

Required

  • Python
  • PyTorch
  • Hugging Face Ecosystem
  • Transformers
  • LLMs
  • Diffusion models
  • Classical ML algorithms
  • Fine-tuning LLMs
  • Optimized inference engines/frameworks
  • Multimodal processing (Text, Image, Video)

Nice to have

  • vLLM
  • TensorRT
  • Knowledge graph

What the JD emphasized

  • 5+ years of relevant experience required
  • Masters' degree or PhD in computer science, statistics, engineering, or other relevant subject area highly preferred

Other signals

  • AI-powered Search, Recommendations and Generative AI-based guidance is a core part of Adobe’s Cloud offering.
  • We are building a new search, recommendations, and knowledge graph platform that powers suits of Adobe product lines.
  • Develop robust methods to evaluate LLM performance, ensuring ethical AI practices that reflect Adobe’s standards