Machine Learning Architect 5 - Genai Experiences

Adobe Adobe · Enterprise · San Jose, CA +1

This role is for a Senior Machine Learning Architect focused on building and shipping AI-powered user experiences within Adobe Experience Cloud. The architect will design and evolve ML systems that blend GenAI and classical ML techniques, focusing on user-centric data, inference pipelines, and evaluation through user behavior. Responsibilities include end-to-end ownership from data to production, defining evaluation frameworks, and driving system reliability and scalability.

What you'd actually do

  1. Build and ship ML-driven capabilities that power AI-assisted user experiences across Adobe Experience Cloud, with a strong emphasis on usability, trust, and proactivity.
  2. Design and architect ML systems that blend multiple approaches—including LLMs, classical ML/NL/IR, and heuristics—to solve complex user-facing problems at scale.
  3. Develop and evolve agentic and reasoning-based systems that integrate retrieval, context, workflows, and decisioning in service of grounded, high-quality user experiences.
  4. Define and own evaluation frameworks that incorporate UX-relevant signals such as relevance, latency, consistency, visual quality, and human feedback—not just offline accuracy metrics.
  5. Drive system reliability, scalability, and performance for user-facing ML systems, including real-time and edge inference, experimentation, and monitoring under strict latency, privacy, and compute constraints.

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • HuggingFace
  • user-centric data pipelines
  • experimentation frameworks
  • production evaluation
  • LLMs
  • retrieval-augmented generation
  • prompt engineering
  • context engineering
  • agent creation
  • agentic systems

Nice to have

  • on-device ML
  • edge ML
  • model optimization
  • human-in-the-loop systems
  • annotation
  • feedback
  • calibration
  • explainability
  • interruption management
  • UI development
  • user experience workflows
  • React
  • CSS
  • Nodejs

What the JD emphasized

  • architecting ML systems end-to-end
  • delivering AI/ML solutions that directly impact user experience
  • experience with LLMs, retrieval-augmented generation, prompt and context engineering and agent creation/agentic systems

Other signals

  • architecting ML systems end-to-end
  • delivering AI/ML solutions that directly impact user experience
  • experience with LLMs, retrieval-augmented generation, prompt and context engineering and agent creation/agentic systems