AI Research Engineer

MOZAT PTE. LTD.; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A leading AI research firm in Singapore is seeking an AI Research Engineer to develop LLM-based and multimodal agent architectures. You will work on next-generation AI systems that reason, adapt, and collaborate with humans. The ideal candidate holds an advanced degree in Computer Science or a related field and has strong Python skills along with hands-on experience in AI tools and frameworks. Competitive salary and flexible working arrangements are offered.

Benefits

Competitive salary

Performance bonus

Equity options

Flexible working arrangements

Opportunities to publish or contribute to open-source

Qualifications

• Proficient in algorithms and data structures.
• Familiarity with multimodal models and AI pipelines.
• Experience with fine-tuning and agent workflows.

Responsibilities

• Design and optimize LLM-based agent architectures.
• Conduct experiments in prompt engineering and reasoning.
• Prototype interactive AI agents for goal-directed actions.

Skills

Python

LLM tooling and APIs

Multimodal models

Analytical mindset

Python

PyTorch

LLM tooling and APIs

Education

Master’s or PhD in Computer Science or related fields

Tools

PyTorch

OpenAI API

LangChain

vLLM

Job description

🧠 About the Role

We are building intelligent systems that can see, speak, understand, and act. As an AI Research Engineer, you will work at the frontier of LLM-based agents and multimodal AI, helping us design and deploy interactive systems that reason, adapt, and collaborate with humans.

You’ll join a fast-moving team of researchers and engineers working on next-generation agentic architectures, exploring how LLMs can use tools, remember, plan, and respond dynamically across language, vision, and other sensory inputs.

This is not just model tweaking. You’ll design full-stack AI behaviors — from prompt design to memory systems to multimodal grounding — that push the boundaries of real-world AI applications.

🔧 Key Responsibilities

• Design, develop, and optimize LLM-based and multimodal agent architectures, integrating language, vision, audio, and structured data.
• Conduct experiments in prompt engineering, fine-tuning, RAG (retrieval-augmented generation), multimodal fusion, and reasoning.
• Prototype interactive AI agents that perceive context, understand intent, and perform goal-directed actions.
• Work closely with engineering and research teams on model evaluation, pipeline design, and large-scale experimentation.
• Keep abreast of the latest advancements in LLMs, multimodal models, and agentic workflows.
• Contribute to technical documentation, internal research reports, and (optionally) external publications.
• Collaborate cross-functionally with product, design, and infrastructure teams to bring cutting-edge AI into production.

✅ Requirements

• Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or related fields.
• Solid foundations in algorithms, data structures, and system architecture.
• Proficient in Python with hands-on experience using PyTorch or similar frameworks.
• Experience with LLM tooling and APIs (e.g., Hugging Face Transformers, LangChain, vLLM, OpenAI API, etc.).
• Familiarity with multimodal models (vision-language, audio-language, video-language) and integrating them into AI pipelines.
• Practical experience in fine-tuning, RAG, or agent workflows (e.g., tool-use, memory, planning).
• Strong analytical mindset and ability to reason from data and experiments.

🌟 Nice to Have

• Publications or open-source contributions in LLM, multimodal, or agentic AI.
• Experience with vector databases, knowledge graphs, or reasoning modules.
• Hands-on work building interactive AI apps (e.g., virtual assistants, autonomous agents, research copilots).
• Understanding of reinforcement learning, LLM evaluation frameworks, or human-AI interaction design.

🎁 What We Offer

• Work at the bleeding edge of AI research and real-world deployment.
• Collaborate with world-class talent across AI, product, and design teams.
• Competitive salary, performance bonus, and equity options (for qualified candidates).
• Flexible working arrangements and opportunities to publish, present, or contribute to open-source.
• A mission-driven environment building AI that understands and helps humans — not just answers queries.