AI Research Intern, Singapore

Jain Global LLC; Singapore; On-site; SGD 20,000 - 33,000; Full time

Job summary

Jain Global LLC in Singapore is seeking an AI Research Intern to design and implement AI-driven systems, focusing on LLMs, agents, and data pipelines. You will work with researchers to prototype and stress-test AI workflows and evaluate model performance on real-world tasks.

The role emphasizes hands-on development, experimentation, and collaboration across research and engineering teams in a fast-paced hedge fund environment.

Qualifications

• Pursuing or recently completed degree in CS/AI/Finance with technical depth.
• Experience building/modifying AI/LLM-based systems or agents.
• Strong programming skills in Python or other languages.
• Familiarity with data handling, storage, and pipelines.

Responsibilities

• Design and build AI-driven systems and workflows.
• Implement LLM pipelines, agent architectures, and supporting infra.
• Extend open-source frameworks for internal use cases.
• Prototype research tools and evaluate robustness in experiments.

Skills

Python

C++

Java

LLM frameworks

SQL

Git

Education

Bachelor’s, Master’s or PhD in CS/AI/Math/Engineering/Finance with technical depth

Tools

SQL

Git

APIs

Job description

## AI Research Intern, SingaporeApply: Singapore Office: Full time: Posted 30+ Days Ago: JR100475## Job Description:Jain Global is a global multi-strategy hedge fund which began trading in 2024, with offices in New York, Houston, London, Hong Kong, and Singapore. We operate across Macro, Fundamental Equities, Equity Arbitrage, Systematic, Credit, and Commodities, with APAC as a regional multi-strategy platform. Our teams combine rigorous research, technology, and engineering across a broad range of domains and workflows.We are hiring AI Research Interns to build and evaluate AI-driven systems and applications across a variety of domains. The role focuses on implementing, extending, and stress-testing modern AI systems, including LLMs and agent-based frameworks, in practical real-world settings.You will work closely with researchers and developers on implementation-heavy projects involving unstructured data processing, workflow automation, experimentation, and evaluation of LLM-based systems for practical research and engineering use cases.The work is highly iterative and hands-on. Typical tasks include developing pipelines, analysing model behaviour, debugging failure modes, extending open-source frameworks, and evaluating robustness through testing and statistical analysis.Example areas include:* Implementing and extending AI agent workflows and automation systems* Structuring, normalizing, and querying unstructured datasets (documents, reports, text)* Extending and adapting open-source AI frameworks to internal use cases* Prototyping research and data-processing tools* Evaluating model outputs for robustness, consistency, and practical usefulness**Responsibilities*** Design, build, and iterate on AI-driven systems and workflows* Implement LLM pipelines, agent architectures, and supporting infrastructure* Extend and modify existing open-source frameworks where appropriate* Design and run experiments to evaluate robustness and real-world applicability* Work with structured and unstructured datasets**Background & Essential Qualifications*** Currently pursuing (or recently completed) a Bachelor’s, Master’s, or PhD in: + Computer Science, AI/ML, Mathematics, Statistics, Engineering + Quantitative Finance, or Finance with strong technical depth and interest in AI* Strong programming ability in one or more of Python, C++, Java, etc.* Experience building and modifying systems involving LLMs, AI agents, or related technologies* Familiarity with software development fundamentals, including: + Data handling and storage (e.g., SQL, databases, data pipelines) + Writing maintainable, modular code + Version control and collaborative development workflows* Strong problem-solving and analytical skills**Preferences*** Familiarity with quantitative research, financial markets, or research-driven environments such as hedge funds or trading firms* Familiarity with structured financial, market, or time-series datasets