Agentic AI Scientist for Financial Services

A*STAR RESEARCH ENTITIES

Singapore

On-site

SGD 120,000 - 180,000

Full time

4 days ago
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Job summary

A*STAR Institute of High Performance Computing (IHPC) is seeking AI Scientists to design and develop agent-based AI systems for financial applications, combining LLM reasoning with structured workflows to address complex problems while ensuring transparency and trustworthiness.

You will collaborate with researchers and industry partners to advance evaluation frameworks, publish in top AI/ML venues, and translate research into deployable systems that meet real-world constraints.

Qualifications

  • PhD in Computer Science, AI, ML, Statistics, or related field.
  • Research and hands-on experience with Agentic AI.
  • Deep expertise in large language models (LLMs) and foundation models.
  • Strong publication record in top-tier AI/ML conferences or high-impact journals.

Responsibilities

  • Lead the design and implementation of agentic AI systems for financial services applications.
  • Investigate and advance methods to improve reasoning accuracy, controllability, explainability, and safety in agent-based architectures.
  • Publish original research in top-tier AI and machine learning venues (e.g., ACL, EMNLP, NeurIPS, ICML, ICLR, AAAI).
  • Collaborate with cross-functional internal teams and industry partners to align agentic AI solutions with real-world financial constraints and requirements.

Skills

Agentic AI
LLMs foundation models
Publication track record

Education

PhD in Computer Science/AI/ML/Statistics

Job description

A*STAR Institute of High Performance Computing (IHPC) is seeking AI Scientists to design and develop agent-based AI systems for financial applications, combining LLM reasoning with structured workflows to address complex problems while ensuring transparency and trustworthiness.

You will collaborate with researchers and industry partners to advance evaluation frameworks, publish in top AI/ML venues, and translate research into deployable systems that meet real-world constraints.

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