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213 of 472 matching · page 3 of 5

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TypeAgent infrastructure126 Agent engineering153 Developer tooling2 Applied research11 Forward deployed179 Other1

ModeRemote92 Hybrid95 On-site72 Location TBD213

LevelJunior18 Mid-level101 Senior216 Staff26 Principal17 Lead20 Manager18 Level TBD56

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213 of 472 matching · page 3 of 5

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Agent engineeringSeniorTop pickNew

AI Agent Algorithm Engineer

ShopeeSingapore, Singapore

Posted 1d ago/Full-time/Expires Nov 10, 2026

Apply on company site

Why it matters

The role owns core agent behavior across planning, multi-agent collaboration, memory, tool execution, MCP integrations, and evaluation. It also combines agent product work with model post-training, reward modeling, long-context memory architecture, and multimodal retrieval systems.

About the job

Job Description

Build core Agent logic, including but not limited to task planning and orchestration, tool calling, multi-turn dialogue management, memory, RAG, context engineering, and multi-agent collaboration.
Lead Continuous Pre-training and Post-training for vertical domains and business scenarios, including building high-quality datasets and data pipelines, designing RL reward models, improving instruction following and reasoning capabilities, task completion, role-playing, anthropomorphic and personalized dialogue, proactive/reactive immersive multimodal conversation experiences, and enhancing the model's IQ and EQ.
Build long-term and short-term memory architectures, addressing issues such as forgetting and attention dispersion in long contexts, and improving immersion and consistency in long-term user interactions.
Build multimodal RAG systems, including development and optimization of key modules such as recall, ranking, long-text processing, and multi-document synthesis.
Develop the Agent's tool layer, integrating external APIs and MCP such as search, code interpreters, browsers, sandboxes, and third-party services.
Design and tune prompts and context management, with tailored optimization for different product requirements.
Design scientifically rigorous quantitative evaluation systems and plans aligned with product requirements; continuously monitor product metrics and provide guidance for Agent and model optimization.
Explore innovative AI applications.

Requirements

Master's degree or above in Artificial Intelligence, Computer Science, Mathematics, or a related field.
At least 2 years of full-time industry experience building and deploying production multi-agent LLM systems (task planning, orchestration, tool calling).
Hands-on experience fine-tuning LLMs via SFT and DPO, combined with hands-on experience building and optimizing RAG/retrieval systems (recall, ranking, embedding fine-tuning).
Good programming skills; proficient in Python
Good problem solving analysis and resolution skills; sustained interest and curiosity in frontier AI technologies and applications; strong self-drive; able to collaborate closely with teams to drive a full closed loop from research to deployment.
Good development experience with Agent frameworks such as LangGraph, Google Agent Development Kit, OWL, or AutoGen.

Stack

python
rag
mcp
langgraph
google agent development kit
owl
autogen

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