Applied AI Intern: Multimodal Vision for Gaming

Razer; Singapore; On-site; SGD 20,000 - 33,000; Part time

Job summary

Razer is seeking an Applied AI Intern to support QAC Vision and Agentic AI features, focusing on evaluating multimodal models and agentic workflows for gaming use cases. Intern will collaborate with AI, Software Engineering, and Product teams to experiment with emerging approaches, understand failure modes, and validate solutions for product adoption.

The role involves building evaluation workflows, benchmarking model quality and agent performance, and documenting findings to guide feature

Qualifications

• Understanding core AI/ML concepts and evaluation metrics.
• Experience with vision or multimodal AI or GenAI.

Responsibilities

• Support the evaluation and benchmarking of vision, multimodal, and agentic AI approaches for QAC use cases using curated datasets.
• Build, maintain, and optimise evaluation workflows to measure model quality, reasoning accuracy, reliability, and agent performance.
• Implement experimentation workflows to evaluate emerging AI models and frameworks, conduct error analysis, and investigate edge cases across perception, reasoning, and agent execution.
• Develop reusable benchmarking utilities, evaluation tools, and prototypes to support technical validation and product buildout.
• Document evaluation findings, limitations, and failure modes, and translate results into actionable recommendations for feature development.
• Conduct research on emerging AI, multimodal, GenAI, and agentic AI technologies to identify potential opportunities for adoption.

Skills

Python

SQL

Analytical thinking

Effective communication

Curiosity

Education

Pursuing BS/MS in CS/AI/ML or related field

Tools

Docker

Kubernetes

FastAPI

NodeJS

Job description

Razer is seeking an Applied AI Intern to support QAC Vision and Agentic AI features, focusing on evaluating multimodal models and agentic workflows for gaming use cases. Intern will collaborate with AI, Software Engineering, and Product teams to experiment with emerging approaches, understand failure modes, and validate solutions for product adoption.

The role involves building evaluation workflows, benchmarking model quality and agent performance, and documenting findings to guide feature