AI Labs Rotational Fellowship, Emerging Talent

The Experience

Build at enterprise scale. Launch like a founder.

Salesforce AI Labs is seeking exceptional emerging AI and computer science talent for our highly selective, 24-month rotational fellowship designed to develop the next generation of AI builders.

Across four six-month rotations, Fellows will tackle real technical and customer challenges, build with frontier AI technologies, and ship solutions into production at enterprise scale. You’ll work alongside experienced researchers, engineers, and technical leaders, with access to AI-native development tools and large scale compute resources - while taking meaningful ownership of what you build.

As a Fellow, you’ll gain hands-on experience across four distinct dimensions of the AI lifecycle, rotating through:

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

The rotational model will give you exposure to different AI teams, technologies, and problems while building the technical depth, adaptability, and product mindset needed to become an AI leader. Upon successful completion of the fellowship, Fellows will transition into high-impact AI teams across Salesforce.

Deadline to apply

November 6, 2026

What You'll Actually Be Doing

• Build and Ship AI Solutions: Design, prototype, and deploy AI-powered applications, agents, and enterprise solutions that solve real-world challenges.
• Work Across AI Labs: Collaborate with teams developing cutting-edge AI capabilities, including AI agents, process generation, and conversational AI.
• Partner with Enterprise Customers: Work alongside field, product, and engineering teams to build, customize, and deploy AI solutions for real customer use cases.
• Own End-to-End Development: Take ideas from concept through experimentation, development, and production deployment, with meaningful ownership of technical outcomes.
• Shape the Future of AI Products: Translate customer insights and technical discoveries into product improvements, new capabilities, and innovative AI experiences.

You're Our Person If...

• Computer Science & Engineering Fundamentals: Currently pursuing or have completed a bachelor’s or master’s degree in Computer Science, Computer Engineering, Data Science, Artificial Intelligence, or a related technical discipline. Strong foundation in data structures, algorithms, software engineering, and technical problem solving.
• Practical Coding Experience: Experience developing software through academic projects, research, internships, professional work, or independent technical projects.
• Programming: Proficiency in Python and experience building software, AI, or machine learning applications.
• AI & ML Foundations & Development: 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.
• Communication: Strong written and verbal communication skills with the ability to explain technical concepts clearly to enterprise stakeholders and external clients.

Even Better If...

• Proven Build History: Portfolio evidence of building, deploying, or open-sourcing software or AI applications through projects, research, hackathons, internships, or public repositories
• Applied AI Experience: Up to 2 years of internship or professional experience in software engineering, data engineering, machine learning, or solutions architecture with demonstrated experience handling production environments and real-world datasets.
• Cloud & Data Pipelines: Exposure to cloud infrastructure, scalable data processing frameworks (Spark, SQL), or MLOps pipelines for model deployment.