About Us
At Aye, we are building the biometric identity layer for the physical world. Our platform transforms everyday human interaction into seamless payments, loyalty, and personalization — without hardware or app friction. We’re backed by major retail and payments players in Asia and are scaling rapidly across the region.
The Role
We are seeking a highly skilled AI/ML Engineer (Multimodal & Applied Research) to drive the design, training, and deployment of advanced machine learning systems that power our face recognition, personalization, and tokenization stack. This role requires a strong foundation in multimodal AI, deep learning architectures, and production-grade ML engineering, with the ability to bridge research innovation and real-world deployment on cloud-native infrastructure.
What You’ll Do
- Research, prototype, and optimize multimodal AI models (vision, behavior, transactions) for real-time decision-making in retail environments.
- Deploy, scale, and monitor ML pipelines on Google Cloud Platform (GCP), leveraging Kubernetes and microservices for high-availability systems.
- Implement personalization and recommendation systems that integrate biometric identity, behavioral patterns, and tokenized data.
- Collaborate with product and engineering teams to translate business requirements into robust AI-driven solutions.
- Maintain end-to-end responsibility for model lifecycle: data acquisition, preprocessing, training, validation, deployment, and performance monitoring.
- Explore cutting-edge AI research and adapt findings into production-ready innovations.
What We’re Looking For
- Strong proficiency in Python, TensorFlow/PyTorch, and modern ML frameworks.
- Solid background in deep learning, computer vision, and multimodal AI architectures.
- Hands-on experience with cloud-native ML deployment (preferably GCP), Kubernetes, and microservices.
- Knowledge of data engineering pipelines and MLOps practices.
- Familiarity with tokenization, secure identity systems, or recommender systems is a strong plus.
- Ability to balance applied research rigor with fast-paced production delivery.
- Minimum 3–5 years in AI/ML engineering, with prior experience shipping AI systems into production.
How to Apply
- Your CV or résumé
- A 1-page note on why you’d like to build with us
- (Optional but awesome): GitHub, Kaggle, portfolio, or any project links that showcase your work