The Opportunity at Adobe, we’re reimagining how businesses connect with their customers — and we want you to be part of it. As a Machine Learning Engineer Interns on the Digital Experience team, you’ll apply innovative AI and ML techniques to power next-generation marketing, personalization, and decisioning systems. You’ll collaborate closely with engineers, and product managers to build intelligent, autonomous products that shape how brands understand and engage their audiences.

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This internship is fast-paced, creative, and high-impact — with opportunities to contribute to real-world AI products used by millions. Many of our past interns have seen their work integrated into production systems at Adobe.

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What You’ll Do

• Advance Adobe AI: Push the boundaries of Adobe’s AI solutions by improving model performance or building high-impact AI products for marketing, personalization, and customer experience.
• Build, Scale, and Deploy ML Systems: Design, develop, and scale applications powered by predictive and generative models — including multimodal autonomous agents and agentic workflows, predictive recommendation and personalization models for adaptive, collaborative decision-making,
• Operationalize ML: Apply ML and GenAI Ops guidelines to create scalable, reliable, and efficient AI/ML workflows.
• Own the End-to-End Lifecycle: Contribute to architecture, experimentation, deployment, and production operations.
• Shape Model Strategy: Analyze data and model performance to recommend the right algorithms, evaluation metrics, and governance approaches.
• Collaborate and Innovate: Partner across research and engineering teams to translate innovative ideas into production-ready solutions.

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What You’ll Bring

• Enrolled in a Bachelor’s, Master’s or PhD program in Computer Science, Applied Mathematics, Statistics, or a related STEM field
• Experience or research in one or more of the following: LLMs, NLP/NLU, Computer Vision, Information Retrieval, Context Engineering, ML Architecture, Optimization, Agent Systems, or Runtime Performance.
• Strong foundation in statistical modeling, machine learning, and data analytics, with hands-on experience solving real-world problems.
• Experience building or scaling data pipelines or ML systems.
• Familiarity or interest in Agent Orchestration and A2A communication frameworks is a plus.
• Proficiency in Python, Scala, Java, or SQL.
• Hands-on experience with ML frameworks such as Hugging Face, Ray, scikit-learn, SparkML, TensorFlow, or PyTorch.
• Excellent communication, collaboration, and problem-solving skills.

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