AI/ML Graduate Engineering Intern (Summer 2026)

Sigma Computing•San Francisco, CA

43d•$70 - $90•Onsite

About The Position

At Sigma, we're not just adding AI-we're building the future of how people work with data. Our platform already lets users explore billions of rows of data in seconds with a spreadsheet-like interface, analyze and present their data in workbooks, and build data apps and workflows. Now we're pushing further, applying AI to reshape how people build in Sigma, discover insights, and make smarter decisions-fast. That's where you come in. As an AI/ML Graduate Engineering Intern, you'll join a growing team focused on building the AI foundation that will power Sigma for the future. Your work will become an integral part of the workflow for the thousands of enterprises that run on Sigma. We're seeking current graduate degree students that are excited about bringing their knowledge and passion for AI/ML to our product and that are excited about launching their careers with our AI team.

Requirements

Current Graduate student studying AI/ML or CS with a focus in AI at a U.S. accredited university
Solid knowledge of machine learning, deep learning, and applied AI
Baseline understanding of building and deploying production-grade AI/ML systems
Knowledge of the full ML lifecycle: data curation, training, deployment, monitoring

Nice To Haves

Experience adapting or training foundation models (language or multimodal) for novel domains a plus
A track record of building -whether it's recommendations, search, machine translation, or something equally complex that demonstrates your domain knowledge
You've built agents that can plan, reason, and use tools
You know your way around cloud infrastructure (AWS, GCP, Azure)
You've worked in a fast-moving startup or high-growth environment

Responsibilities

Work alongside and learn from our growing AI/ML team
Partner with product, design, and engineering teams to identify high-impact AI/ML opportunities and support the usage of AI in our product and internal development processes
Prototype and productionize AI systems that feel intuitive but do a lot under the hood-recommendations, natural language interfaces, agentic workflows, and more
Contribute to the development of an AI/ML infrastructure that powers both internal tooling and customer-facing features
Tackle novel UX problems at the intersection of AI, BI, and apps