Help build platform capabilities by packaging algorithms, models, operators, and tools into standardized, callable components.
Help build automated model training pipelines, deploy and maintain AI model services, and contribute to testing, releases, and troubleshooting.
Help design and implement web frontend, backend, and user interfaces, and integrate APIs.
Help build automation with AI agents, including automatic operator composition, screening and selection of algorithms and data, and tool invocation.
Contribute to data ingestion, processing, storage, and task scheduling capabilities.
Contribute to continuous improvements in system performance, reliability, and maintainability.
Requirements
Currently in the later years of a bachelor’s degree, or pursuing a master’s degree or PhD, in computer science, software engineering, or a related field. At least six consecutive months of internship availability is mandatory; short internships are not offered. Availability on site for at least four days per week is preferred.
Strong programming skills: proficiency in Python and web backend development, with knowledge of frontend development in a framework such as React or Vue and frontend/backend API integration.
Proficiency with AI agent tools such as Codex or Claude Code, or experience building and using your own agents. You are welcome to demonstrate a project, experiment, or demo created in collaboration with AI during the interview.
Substantial experience in at least one of the following areas (one is sufficient; experience in all areas is not required):
Full-stack projects: demonstrable end-to-end personal, course, or open-source projects, with experience taking requirements through deployment and operation.
AI application development: applications built with LLMs, VLMs, or AI agents, and familiarity with common patterns for tool calling and workflow orchestration.
Model training and deployment: practical experience training, tuning, or deploying AI models as services, and an understanding of the dependencies between models, data, and applications.
Engineering infrastructure: familiarity with Linux, containers, databases, and common deployment tools, with practical experience in GPU task scheduling, inference optimization, or MLOps.
A focus on automation, documentation, and collaboration, with the flexibility to take on different engineering tasks as needed in a startup.
Preferred qualifications
Complete examples of AI applications, AI agent products, or open-source tools you have built.
Knowledge of quantum computing or experience integrating and using quantum computing platforms.
How to apply
Please specify the role you are applying for and include your résumé, availability, and links to work or projects that demonstrate your skills.
Please state the internship duration and number of days per week you can commit to.
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