Chief AI Scientist: Foundation Models & Global Scale

UMELIFE (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 240,000 - 360,000; Full time

Job summary

UMELIFE (SINGAPORE) PTE. LTD.

is seeking a Chief Scientist – Foundation Models to lead our core AI research and development, define the foundation model technology roadmap, and drive turning cutting-edge research into scalable products for global markets. The role requires 8+ years of AI and large language model experience, deep hands-on across foundation-model lifecycles (data, architecture, pre/post-training, alignment, inference), and a proven ability to build an international AI research

Qualifications

• Master’s degree or above in Computer Science, AI, ML, or related field.
• 8+ years of AI and large language model research & development experience.
• Experience at leading global AI/tech companies and on foundation-model programs.
• Deep hands-on across data, architecture, pre/post-training, alignment, and inference.
• Strong track record in training well-known foundation models with impact.
• Global perspective and ability to build international AI teams.

Responsibilities

• Define the company’s foundation model technology roadmap.
• Lead AI research and development across foundation models.
• Drive transition of research into scalable products and deployments.
• Build and lead an international, world-class AI research team.
• Collaborate with product and business teams to translate capabilities into applications.

Skills

Foundation Models

LLMs

Pre-training

Post-training

Reasoning

RLHF

Inference optimization

Technical leadership

Education

Master’s degree in Computer Science / AI / ML or related field

Job description

UMELIFE (SINGAPORE) PTE. LTD.

is seeking a Chief Scientist – Foundation Models to lead our core AI research and development, define the foundation model technology roadmap, and drive turning cutting-edge research into scalable products for global markets. The role requires 8+ years of AI and large language model experience, deep hands-on across foundation-model lifecycles (data, architecture, pre/post-training, alignment, inference), and a proven ability to build an international AI research