Research Engineer (CFAR), IHPC

A*STAR RESEARCH ENTITIES; Singapore; On-site; SGD 80,000 - 100,000; Full time

Job summary

A research organization in AI in Singapore is seeking a motivated Research Engineer to advance trustworthy AI, emphasizing privacy-preserving methods for adapting large language models. Candidates will conduct cutting-edge research, tune LLMs, and contribute to open-source projects. A degree in Computer Science or a related field, strong programming skills in Python, and experience with LLMs are required. Applications can be sent to provided emails.

Qualifications

• Degree in Computer Science, Machine Learning, or related field.
• Strong background in deep learning, natural language processing, or large-scale optimization.
• Demonstrated experience in working with open-source LLMs.

Responsibilities

• Conduct research and development of LLM tuning paradigms.
• Contribute to privacy preserving LLM tuning experiments.
• Publish high-impact research in top-tier venues.

Skills

Deep learning

Natural language processing

Strong programming skills in Python

Familiarity with ML frameworks

Experience with open-source LLMs

Good communication skills

Education

Degree in Computer Science, Machine Learning, or related field

Tools

PyTorch

HuggingFace Transformers

Job description

A*STAR Centre for Frontier AI Research (A*STAR CFAR) is seeking a motivated and skilled Research Engineer to join our team focused on advancing trustworthy AI, with a specific emphasis on privacy-preserving methods for adapting large language model (LLM) to downstream tasks. The successful candidate will contribute to cutting-edge research on how to effectively tune or adapt LLMs without compromising both data privacy and model privacy.

Key Responsibilities

Successful candidates will be responsible, but not limited to:

• Conduct research and development of LLM tuning paradigms.
• Directly contribute to experiments of privacy preserving LLM tuning, including designing experimental details, writing reusable code, running evaluations, and organizing results.
• Directly contribute to Agentic AI of code generation, compilers, debugging, etc.
• Evaluate utility, privacy, and efficiency trade-offs among compared baselines.
• Publish high-impact research in top-tier venues. (e.g., NeurIPS, ICLR, ACL, IEEE S&P).
• Contribute to open-source tools and frameworks; and potentially guide junior researchers or interns.

Requirements

• Degree in Computer Science, Machine Learning, or related field.
• Strong background in deep learning, natural language processing, or large-scale optimization.
• Demonstrated experience in working with open-source LLMs. (e.g., fine-tuning, instruction tuning, prompt engineering)
• Familiarity with privacy-preserving machine learning concepts. (e.g., federated learning, synthetic data).
• Strong programming skills in Python and experience with ML frameworks. (e.g., PyTorch, HuggingFace Transformers).
• Good written and verbal communication skills.

Please submit your CV, a short research statement (if available) to [e-mail] and [e-mail].