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.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.
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.
What You’ll Bring
- Enrolled in an MS or PhD program in Computer Science, Applied Mathematics, Statistics, or a related STEM field with an expected graduation date of December 2026 – June 2027.
- 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.
