Associate Data Scientist · Razer

Tue, 28 Apr 2026 14:08:51 GMT
Engineering

Razer · Singapore · onsite

Joining Razer will place you on a global mission to revolutionize the way the world games. As an Associate Data Scientist, you will play a critical role in developing autonomous and semi-autonomous multimodal AI agents that integrate large language models (LLMs), multimodal foundation models (vision, speech, audio), retrieval systems, fine-tuned models, and tool-based orchestration to enable intelligent, real-time, and context-aware capabilities across Razer's gaming and platform experiences.

The ideal candidate is a strong AI systems and applied research engineer with hands-on experience in multimodal agent architectures, RAG pipelines, LLM and multimodal model fine-tuning, and production deployment. You will work across the full lifecycle,from data preparation and multimodal model adaptation to system integration, deployment, and continuous optimization,while collaborating closely with AI Software Engineers, Research Scientists, Platform Engineers, and DevOps teams.

Key responsibilities include designing, implementing, and maintaining multimodal agentic AI architectures, researching and building multimodal agents that combine text, vision, audio, and speech models for grounded understanding and interaction, building, operating, and optimizing multimodal Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector databases, and internal multimodal knowledge sources (text, images, video, audio), and performing LLM and multimodal model fine-tuning and adaptation to improve reasoning, perception, and task performance.

You will also develop internal agent frameworks, multimodal tooling, and orchestration layers for LLM- and multimodal-model-driven workflows, integrate and adapt 3rd-party multimodal AI services (LLMs, vision models, speech/audio models, agent platforms) into agent-based systems, prototype, evaluate, and productionize multimodal agent frameworks and research ideas, balancing model capability, latency, cost, and system complexity, deploy and operate production-grade multimodal AI systems, addressing scalability, latency, reliability, observability, and cost controls, conduct benchmarking and evaluation of multimodal models, agent behaviors, fine-tuning strategies, and retrieval approaches, collaborate with platform, infrastructure, and security teams to ensure secure, compliant, and maintainable AI systems, and stay current with advances in multimodal foundation models, agentic AI research, RAG methods, and deployment patterns.