AI Software Engineer Intern

What you will do

Job Title: AI Software Engineer Intern
Location: Remote
Type: Freelance / Contract-Based

Hours: Part-time / Full-time

Salary: SGD 500-1000

Job Description

We are seeking an AI Software Engineer to build reliable, production-grade AI features and systems across our venture portfolio. You will work on the end-to-end stack that powers AI-enabled products, including model integrations, backend services, data pipelines, evaluation harnesses, and safety and reliability tooling.

Key Responsibilities

• Build AI-enabled features and internal tools that improve user value and team execution.
• Implement model and tool integrations (LLMs, vector DBs, search, workflow automation) with strong reliability and security practices.
• Design and maintain evaluation workflows to measure quality, correctness, latency, and cost.
• Improve system observability and guardrails to ensure safe, consistent behavior in production.
• Collaborate cross-functionally to translate product needs into robust technical solutions.

Why Apply

You will work close to real product problems, ship AI features end-to-end, and develop strong judgment for what it takes to make AI systems reliable in production.

Location

The position is fully remote, offering you the flexibility to work from wherever you choose. Whether it's the comfort of your home office, a favorite café, or a cozy corner of the world, you have the freedom to create your ideal workspace. Join our team from anywhere in the world and be part of a dynamic, interconnected community.

Start Date

Our program is adaptable: We offer ongoing recruitment, allowing internships and placements to start at any time, tailored to the student's needs.

Apply here: www.horizonlabs.ai

What we are looking for

Preferred Qualifications

• Strong software engineering fundamentals with experience shipping production systems.
• Proficiency in Python and at least one backend language (e.g., TypeScript/Node.js, Go, or Java).
• Experience integrating LLM APIs or open-source models, including prompt design and structured output patterns.
• Familiarity with ML/AI infrastructure concepts such as embeddings, vector search, retrieval, and model evaluation.
• Comfort with cloud services (AWS, GCP, or Azure), CI/CD, and observability (logging, metrics, tracing).
• High attention to detail and discipline in testing, monitoring, and documentation.
• Ability to work in ambiguity and iterate quickly with product, design, and operations.