Deep Learning Engineer - Monee

About Monee

Monee is a leading digital payments and financial services provider in Southeast Asia, with a growing presence in Latin America. Its mission is to better the lives of individuals and businesses in the region with financial services through technology.

Monee delivers a comprehensive range of digital financial products and services, including mobile wallets, payment processing, credit, banking, and insurtech. By leveraging technology and strategic partnerships, Monee aims to drive greater digital financial inclusion, convenience, and accessibility for individuals and small businesses across Southeast Asia and Latin America.

Monee is part of Sea Limited, a global technology company.

We are looking for a Deep Learning Engineer to drive the evolution of our risk modeling capabilities. This role goes beyond traditional ML — you will be at the forefront of transitioning production risk systems from classical approaches to modern deep learning architectures, building large-scale pre-trained models, and applying reinforcement learning for decision making.

You will work closely with Risk, Data Science, and Engineering teams to design and deliver next-generation models that power credit decisions at scale.

• Design and implement deep learning models to enhance existing tree-based risk modeling pipelines, with a focus on improved generalization, representation learning, and scalability.

• Develop and optimize large-scale pre-trained models, including architecture design, pre-training strategies, fine-tuning, and inference optimization for production environments.

• Build reinforcement learning systems for dynamic credit limit and interest rate adjustment, including reward shaping, policy optimization, and online learning frameworks.

• Engineer deep learning embedding solutions for heterogeneous data sources (e.g., bureau data), extracting rich latent representations to improve downstream model performance.

• Develop sensitivity modeling for in-loan pricing decisions, capturing complex user-level behavioral responses to rate and limit changes.

• Collaborate with Risk and Data Science teams to translate business problems into DL problem formulations, evaluating modeling trade-offs across accuracy, latency, and fairness.

• Build and maintain end-to-end model pipelines covering training, evaluation, deployment, and monitoring across batch and real-time systems.

• Partner with Data Engineering to ensure feature consistency, data quality, and reliable offline-to-online feature parity.

• Contribute to modeling best practices, reproducibility standards, and internal technical documentation.

• Master's degree in Computer Science, Mathematics, Statistics, or a related quantitative field.

• Minimum 3 years of hands-on experience in applied deep learning or machine learning engineering, with production deployment experience.

• Proficiency in Python and deep learning frameworks (PyTorch preferred; TensorFlow/JAX a plus).

• Strong understanding of neural network architectures including Transformers, sequential models, and embedding-based models.

• Practical experience with at least one of: reinforcement learning (policy gradient, actor-critic), large-scale pre-training / fine-tuning, or representation learning.

• Experience in model monitoring, drift detection, and lifecycle management in production.

• Experience in fintech, credit risk, or financial services is strongly preferred.

• Strong communication skills to collaborate with cross-functional stakeholders across Risk, Product, and Engineering.

Nice to Have

• Experience with large-scale distributed training (e.g., multi-GPU, parameter servers).
• Familiarity with online learning or continual learning systems.
• Exposure to causal inference or uplift modeling for pricing/limit optimization.
• Experience with feature stores or real-time serving infrastructure.