Horizon Labs – Software Engineer (AI Systems) Intern

Horizon Labs is an AI-studio focused on building AI-enabled consumer ventures. We operate like a startup factory with experienced teams, frameworks, and infrastructure and look for real-world problems that have meaningful AI solutions and focus on building within the consumer and healthcare verticals. Our model involves incubating ideas, launching startups, and scaling growth globally.

Job Description

We are seeking a Software Engineer (AI Systems) Intern to build the software and infrastructure that powers AI-enabled workflows and product features. You will work on systems that connect models, tools, services, and product requirements into reliable applications.

Preferred Qualifications

• Currently pursuing or recently completed a relevant degree, ideally from leading universities such as NUS, NTU, or SMU, while still considering strong candidates from other institutions.
• Strong academic performance, ideally distinction or above, alongside evidence of initiative, ownership, and high-quality execution.
• Comfortable using AI tools beyond simple chat workflows to accelerate analysis, drafting, research, and execution.
• High attention to detail and discipline in checking facts, requirements, and deliverables before sharing work.
• Able to thrive in a fast-moving startup environment with ambiguity, rapid iteration, and cross-functional collaboration.
• Strong software engineering fundamentals with interest in systems design, integrations, backend logic, and production reliability.
• Interest in LLM systems, orchestration, APIs, and practical AI infrastructure is a plus.

Key Responsibilities

• Build and maintain application logic, integrations, and infrastructure for AI-enabled systems.
• Work on APIs, orchestration layers, and tooling that connect models with product workflows.
• Collaborate with product and operations stakeholders to turn requirements into reliable system behavior.
• Use AI-assisted engineering tools responsibly to improve development speed, debugging, and documentation.
• Improve observability, reliability, and maintainability across AI-related technical workflows.