AI Full-Stack Engineer

Responsibilities

1. Build platform capabilities by packaging algorithms, models, operators, and tools into standardized, callable components that support delivery at scale.
2. Build automated model training pipelines, deploy and maintain AI model services, and establish testing, release, monitoring, and troubleshooting processes.
3. Design and implement web frontend, backend, and user interfaces. Integrate APIs and turn AI capabilities into usable products and tools.
4. Build automation with AI agents: automatically combine operators, screen and select algorithms and data, invoke tools, and generate optimal workflows for customer tasks.
5. Build data ingestion, processing, storage, and task scheduling capabilities for algorithm experiments and applications.
6. Adapt and validate deployments in customer environments, continuously improving system performance, reliability, and maintainability.

Requirements

1. Strong software engineering fundamentals and demonstrable end-to-end project experience, from requirements through deployment and operation.
2. Proficiency with AI agent tools such as Codex or Claude Code, or experience building and regularly using your own agents. Please demonstrate a project, experiment, or demo created in collaboration with AI during the interview.
3. Familiarity with Python backend development, web frontend development, and frontend/backend API integration.
4. Practical familiarity with AI agents, LLMs, VLMs, and generative models such as diffusion models, including common patterns for tool calling and workflow orchestration.
5. Hands-on experience training, tuning, and deploying AI models as services. An understanding of the dependencies between models, data, and applications, and experience processing large amounts of unstructured data or building data pipelines.
6. Familiarity with Linux, containers, databases, and common deployment tools; practical experience in GPU task scheduling, inference optimization, or MLOps; and the ability to diagnose and solve engineering problems.
7. An end-to-end perspective, connecting frontend and backend interfaces, AI agents, and the training, deployment, and operation of different models.
8. 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