Senior Machine Learning Engineer

Adobe Adobe · Enterprise · San Jose, CA

Senior Machine Learning Engineer at Adobe Journey Optimizer B2B, focusing on AI-powered customer journey orchestration. The role involves training and fine-tuning ML models, architecting ML pipelines, and contributing to the deployment and production operations of ML models and systems. Experience with generative AI, LLMs, fine-tuning, and MLOps is required, with a preference for RAG, agentic workflows, and personalization use cases.

What you'd actually do

  1. Train and fine-tune ML models that solve business use cases and handle data at scale.
  2. Architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust.
  3. Dive deep into data to recommend the right models, evaluation metrics, and governance approaches.
  4. Provide hands-on technical contributions, collaborating with engineers on architecture, implementation, and standard processes.
  5. Engage throughout the product lifecycle in architecture, design, deployment, and production operations of ML models and systems.

Skills

Required

  • 6+ years of experience in machine learning
  • 3+ years of hands-on experience working with generative AI technologies
  • Strong Python
  • deep learning engineering skills
  • experience training and running inference with PyTorch or TensorFlow/JAX
  • Experience with post-training techniques such as fine-tuning, alignment, or distillation
  • Familiarity with deployment tools like Docker, MLOps frameworks, and ML services

Nice to have

  • experience with cloud platforms like Azure and AWS
  • Hands-on experience with retrieval-augmented generation (RAG)
  • semantic embeddings
  • agentic AI workflows
  • ML inference systems for personalization or recommendation use cases
  • Published research, contributions to open-source ML projects, or patents in AI/ML domains
  • Experience with Adobe Experience Platform, Marketo Engage, or Journey Optimizer

What the JD emphasized

  • successful delivery of ML projects
  • generative AI technologies such as LLMs, evaluations, fine-tuning
  • training and running inference with PyTorch or TensorFlow/JAX
  • post-training techniques such as fine-tuning, alignment, or distillation
  • retrieval-augmented generation (RAG)
  • agentic AI workflows
  • ML inference systems for personalization or recommendation use cases

Other signals

  • AI-powered customer journey orchestration
  • predict intent signals
  • deliver hyper-personalized experiences
  • architect and optimize end-to-end ML pipelines
  • generative AI technologies such as LLMs, evaluations, fine-tuning