Company Introduction
ADVANCE.AI is a leading AI company that provides digital transformation, fraud prevention, and process automation solutions for enterprise clients. A leader in Artificial Intelligence, risk management and digital lending solutions, it currently partners over 500 enterprise clients across banking, financial services, fintech, payment, retail and e-commerce sectors. ADVANCE.AI has a global footprint across 11 markets in 3 continents.
ADVANCE.AI is part of Advance Intelligence Group,
a Series D-backed Tech Unicorn valued at US$2 billion, and also one of the largest independent technology startups based in Singapore. Founded in 2016, the Group has presence across Southeast Asia, Latin America and Greater China. The Group is backed by top tier investors SoftBank Vision Fund 2, Warburg Pincus, Northstar, Vision Plus Capital, Gaorong Capital, Pavilion Capital, GSR Ventures and Singapore-based global investor EDBI.
Advance Intelligence Group employees are united by a shared vision and purpose: to
Advance with Intelligence for a Better Life--for our customers, colleagues and communities.
Our culture is built on values that are core to who we are and what we stand for:
- We foster an INNOVATION mindset
- We achieve results with EFFICIENCY and excellence
- We take pride in the QUALITY of our work
- We uphold INTEGRITY in all we do
- We embrace COLLABORATION to work across business lines and borders
About The Role
Join the AI team to research and prototype next-gen models for deepfake detection and document forgery prevention. You will work end-to-end, from data curation to experiments, and collaborate closely with algorithm engineers and scientists to ship measurable improvements.
You Will
- Research and evaluate the latest GenAI techniques (e.g., diffusion/LLM-assisted approaches) to expand deepfake detection coverage across emerging attack types.
- Explore multimodal / vision-language models to build advanced vision intelligence for efficient document forgery detection (IDs, proofs, and supporting documents).
- Own data curation and dataset iteration: sampling strategy, labeling guidelines, quality checks, and hard-case mining.
- Run model experiments: training/inference pipelines, ablation studies, benchmarking, and error analysis.
- Collaborate with algorithm engineers/scientists to drive measurable performance gains and support productionization readiness (latency/robustness considerations).
- Document findings clearly (experiment logs, reports, and recommendations).
What You'll Need To Succeed
- Bachelor Degree in CS/EE/Math/AI or related field.
- Strong foundations in machine learning and computer vision (CNN/Transformers, representation learning)
- Hands-on coding in Python; experience with PyTorch preferred.
- Solid experimental discipline: reproducible training, metrics, and structured reporting.
- Self-driven, curious, and able to learn quickly; strong collaboration and communication skills.
