2027 Intern - Machine Learning Engineer

The Opportunity  

Adobe is looking for Machine Learning Engineer interns to work on some of the most impactful AI systems in the industry — from generative AI features and intelligent agents to search, recommendations, and production ML models used by hundreds of millions of people.  

Depending on your team, you might be building LLM-powered applications, developing multimodal AI systems, designing evaluation frameworks, or deploying models that directly shape how customers experience Adobe's creative and marketing products. The work is applied, the teams are technical, and the projects are real.  

All 2027 Adobe interns will be co-located hybrid, working between their assigned office and home, based where their manager and team are located. This will allow you to get the most support to ensure collaboration and the best employee experience. Managers will determine the frequency you need to go into the office to meet priorities. 

What You’ll Do  

Build and improve machine learning systems — including LLM-based applications, recommendation models, search and retrieval pipelines, or multimodal AI features — depending on your team's focus 

Design, train, evaluate, and iterate on models across the full ML lifecycle, from data preparation and experimentation through deployment and monitoring in production environments 

Contribute to applied AI projects with real product impact, including generative AI features, agentic workflows, and intelligent systems used by Adobe customers at scale 

Collaborate closely with engineers, product managers, and researchers to scope problems, ship working solutions, and communicate findings to both technical and non-technical stakeholders 

What You Need to Succeed  

Currently enrolled full time and pursuing a Bachelor's, Master's, or PhD degree in Computer Science, Computer Engineering, or a related technical field, with an expected graduation date of December 2027 – June 2028 

Strong foundation in machine learning and deep learning concepts, including familiarity with generative AI and large language models (LLMs) 

Strong Python programming skills; familiarity with other languages such as Java or C++ is a plus 

Hands-on experience with machine learning frameworks such as PyTorch or TensorFlow; familiarity with LLM tooling (e.g., Hugging Face, LangChain) or ML libraries such as scikit-learn is a plus 

Exposure to cloud platforms (AWS, Azure, or GCP) or experience with model deployment and evaluation workflows is a plus 

Strong analytical and quantitative problem-solving ability 

Excellent communication skills and ability to work effectively in a collaborative team environment 

Ability to participate in a full-time internship between May–September