Machine Learning Engineer Agentic AI & Personalization

Overview

At Adobe, we’re transforming personalization from static models to autonomous, agent‑driven AI systems. Within the Digital Experience Platform we’re building Agentic Systems that can design, optimize, and continuously improve personalization strategies across billions of interactions. These systems reason over customer data, business goals, and real‑time signals to drive smarter decisions at scale.

• Build LLM‑powered agents for model generation, experimentation, and next‑best‑action optimization
• Develop recommendation, ranking, and real‑time decisioning systems
• Architect end‑to‑end ML systems (feature pipelines, training, low‑latency inference)
• Lead MLOps (CI/CD, monitoring, drift detection, retraining)
• Partner with Product to drive measurable business impact
• Mentor engineers and shape agentic AI best practices

• 5+ years building production ML systems at scale
• Experience in personalization, recommendation systems, or real‑time ML modeling
• Strong skills in Python and ML frameworks (PyTorch, Tensor Flow, Hugging Face, etc.)
• Track record of owning end‑to‑end ML systems and driving impact
• Hands‑on experience with production‑grade LLMs and agents is a plus

• Build AI systems that build and optimize other AI systems
• Work on real‑time personalization at massive scale
• Shape how agentic AI is applied in enterprise products

The U.S. pay range for this position is $151,800 – $265,350 annually. In California, the pay range is $183,300 – $265,350. Pay within this range varies by work location and may also depend on job‑related knowledge, skills, and experience.

Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more.

Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.