Principal Engineer Ai/ml

Adobe Adobe · Enterprise · San Jose, CA

Principal Engineer AI/ML at Adobe to lead GenAI initiatives, architecting multimodal systems and end-to-end AI solutions. The role involves driving innovation in representation learning, LLM/VLM/multi-agent frameworks, and enterprise search, with a focus on building scalable, production-ready services and industry-leading evaluation systems. Requires deep expertise in NLP, CV, DL, Generative AI, LLM fine-tuning, RAG, multi-agent orchestration, and ML platform engineering.

What you'd actually do

  1. Shape the technical vision and roadmap for GenAI initiatives that extract structured insights from brand knowledge and transform them into verified, on-brand content at scale.
  2. Leverage foundational principles of NLP, Linguistics, and Computer Vision to architect multimodal systems that push the frontier of grounded content generation across text, image, and video.
  3. Drive innovation in dense, sparse, and hybrid representation, including embedding models, contrastive learning, metric learning, learning-to-rank, and reinforcement learning to advance retrieval and recommendation at scale.
  4. Architect end-to-end AI systems utilizing LLMs, VLMs, multi-agent frameworks, and enterprise search designed for both offline training and online production workflows.
  5. Implement and adapt techniques from academic research; evaluate algorithmic trade-offs considering data requirements, latency, and runtime.

Skills

Required

  • AI/ML
  • technical leadership
  • NLP
  • Linguistics
  • Computer Vision
  • Deep Learning
  • Generative AI
  • LLM fine-tuning
  • LoRA
  • QLoRA
  • prompt engineering
  • RAG
  • GraphRAG
  • vector databases
  • multi-agent orchestration
  • agentic workflows
  • PyTorch
  • TensorFlow/Keras
  • JAX
  • Hugging Face
  • CUDA
  • Python
  • ML platform engineering
  • MLOps
  • LLMOps
  • distributed training
  • model serving
  • feature stores
  • experiment platforms
  • observability systems
  • AWS
  • GCP
  • Azure
  • data engineering
  • dataset management
  • feature engineering
  • labeling pipelines
  • data drift
  • quality frameworks
  • mentoring senior engineers

Nice to have

  • publications at top-tier AI/ML conferences
  • open-source contributions
  • participation in industry forums
  • building and shipping GenAI-first applications at enterprise scale

What the JD emphasized

  • 14+ years of experience in AI/ML with a strong track record of technical leadership across multiple divisions or product areas, including shaping technical vision and roadmaps for ML organizations.
  • Expert-level experience with LLM fine-tuning techniques (LoRA, QLoRA), prompt engineering, RAG/GraphRAG, vector databases, multi-agent orchestration, and agentic workflows.
  • Extensive experience with ML platform engineering (MLOps/LLMOps) including distributed training, model serving, feature stores, experiment platforms, and observability systems across cloud platforms (AWS, GCP, Azure).
  • Solid grasp of data engineering including dataset management, feature engineering, labeling pipelines, data drift, and quality frameworks at scale.
  • Proven ability to mentor senior engineers and elevate technical standards across teams.

Other signals

  • architect multimodal systems
  • architect end-to-end AI systems
  • design industry-leading evaluation systems
  • implement and adapt techniques from academic research
  • drive architectural decisions