Machine Learning Engineer 4

AdobeSan Jose, CA

About The Position

Adobe is looking for a Senior Machine Learning Engineer to help shape the future of agentic AI in the enterprise. In this role, you will design, build, and scale cutting-edge platforms and products that redefine how enterprises create and optimize customer experiences and marketing campaigns. You will play a key role in advancing AEP Agent Orchestrator—a foundational platform layer that manages and connects Adobe and third-party agents. You will work in a fast-moving, high-impact environment with a team of talent engineers and applied scientists where creativity, collaboration, and data-driven innovation come together to make a real difference.

Requirements

  • PhD or MS degree in Computer Science or related field required.
  • 5+ years of experience in machine learning, including production-scale deployments
  • Experience with agile development, and short release cycles
  • Good understanding of statistical modeling, machine learning, or analytics concepts; ability to quickly learn new skills and work in a fast-paced team.
  • Proficient in one or more programming languages such as Python and Java.
  • Familiarity with cloud development on Azure/AWS.
  • Experience using Relational (MySQL, Postgres) and NoSQL datastores (Redis, ElasticSearch, Snowflake) along with data access patterns and strategies
  • Experience working with at least one deep learning framework such as TensorFlow or PyTorch.
  • Experience with LLMs including prompt/context-engineering, modern LLM APIs, fine-tuning models etc.
  • Experience working with both research and product teams.
  • Excellent problem-solving and analytic skills
  • Excellent communication and relationship building skills.

Responsibilities

  • Design and development of state-of-the-art agentic AI system and platform powered by generative AI, including working on engineering problems such as defining APIs, integrating with UIs, deploying Cloud services, CICD, etc., as well as implementing ML- and LLM-Ops best practices, delivering high quality, production ready code.
  • Design and build ML workflows for enterprise-scale model customization, serving, and ecosystem integration.
  • Partner with researchers and applied scientists on productization of innovations.
  • Engage in the product lifecycle, design, deployment, and production operations.

Benefits

  • comprehensive benefits programs
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