AI Research Engineer — Representation Learning
Core
Develop Transformer-based models for structured and time-series credit risk data with a focus on representation learning and interpretable AI attributes.
Role type
AI Research Engineer (Representation Learning)
Builds
Research prototypes and foundational models for credit risk analytics
Domain
Financial services / Credit risk / Deep Learning
Deliverable
production ML models
Required skills
Transformer architectures, attention mechanisms, sequence modeling, linear algebra, statistics, probability, representation learning, deep learning training dynamics, Python
Preferred skills
sequence-to-sequence models, diffusion models, generative modeling, mechanistic interpretability, irregular time-series handling, embedding visualization
Technologies
PyTorch, TensorFlow
Responsibilities
Build and experiment with transformer-based models for credit data, analyze internal representations (embeddings, latent spaces), conduct rigorous ablation and hyperparameter experiments, develop research prototypes, collaborate on integration with ML engineering, extend models to discriminative and generative architectures
Seniority
Senior, hands-on IC
