AI Labs Summer Internship, Emerging Talent

About the Role

Salesforce AI Labs is seeking exceptional emerging AI and computer science talent for a highly selective, 12-week summer internship designed to develop the next generation of AI builders. As an AI Labs intern, you will spend 12 weeks embedded in one of four disciplines, tackling real technical, product, or customer challenges, building with frontier AI technologies, and contributing to solutions at enterprise scale. You will work alongside experienced researchers, engineers, product managers, and technical leaders, with access to AI-native development tools and opportunities to take meaningful ownership of your work.

Interns will be placed in one of four disciplines:

• Build (Product Engineering): Design, build, and ship AI-powered products, developing technical expertise in production-scale engineering.
• Shape (Product Management): Translate customer needs into product strategy, define priorities, and help shape the next generation of AI products.
• Deliver (FDE / Builders): Work with teams developing and deploying AI solutions that solve real-world customer challenges.
• Transform (DET / Customer Zero): Reimagine enterprise workflows and operations, leveraging AI to drive transformation at scale.

While you will spend the summer gaining hands-on experience in one discipline, you will also collaborate with interns across the other three disciplines on a cross-functional capstone project. Together, you will bring engineering, product strategy, customer solutions, and enterprise transformation perspectives to a shared AI challenge, culminating in a final presentation at Demo Day.

Key Responsibilities

• Build and Deliver AI Solutions: Spend 12 weeks embedded in one AI Labs discipline, contributing to meaningful projects that solve real technical, product, or customer challenges.
• Work with Frontier AI: Gain hands-on experience with AI-native development tools, AI agents, and emerging technologies to accelerate experimentation, development, and problem-solving.
• Collaborate Across Disciplines: Partner with interns from Build, Shape, Deliver, and Transform on a cross-functional capstone project, combining different perspectives to develop an innovative AI solution.
• Own Meaningful Work: Take ownership of assigned projects, from problem definition and experimentation through development and delivery, with guidance from experienced AI Labs leaders.
• Present Your Work: Showcase your team's capstone project at an in-person Demo Day, presenting your solution, approach, and impact to an executive panel.
• Learn from AI Leaders: Engage with engineering, product, FDE, and DET teams through mentorship, workshops, and cohort experiences that broaden your understanding of the AI lifecycle.

Requirements & Tech Stack

• Computer Science & Engineering Fundamentals: Currently pursuing a bachelor's or master's degree in Computer Science, Computer Engineering, Data Science, Artificial Intelligence, or a related technical discipline, graduating between December 2027 and June 2028. Strong foundation in data structures, algorithms, software engineering, and technical problem-solving.
• Practical Coding Experience: Experience developing software through academic projects, research, internships, or independent technical projects.
• Programming: Proficiency in Python and experience building software, AI, or machine learning applications.
• AI & ML Foundations: Understanding of core AI/ML concepts and experience using modern AI development tools to accelerate experimentation, development, and problem-solving.
• Builder Mindset: Demonstrated curiosity, high ownership, and ability to solve complex problems in fast-moving, ambiguous environments.
• Collaboration & Communication: Strong written and verbal communication skills, with the ability to collaborate across disciplines and explain technical concepts clearly to both technical and non-technical audiences.

Even Better If

• Proven Build History: Portfolio evidence of building, deploying, or open-sourcing software or AI applications through projects, research, hackathons, or public repositories.
• Applied AI Experience: Previous internship, research, or project experience in software engineering, data engineering, machine learning, product development, or solutions architecture.
• Cross-Functional Experience: Experience working across engineering, product, design, or customer-facing teams to solve complex problems.
• Cloud & Data Pipelines: Exposure to cloud infrastructure, scalable data processing frameworks (Spark, SQL), or MLOps pipelines for model deployment.