Deep Learning Research Engineer Intern
Core
Building a multi-target joint probabilistic foundation model for temporal forecasting, tabular regression/classification, and mixed-modality inputs to solve real-world business problems.
Role type
Post-doctoral level research-engineering intern
Builds
Probabilistic foundation models with coherent joint structure across variables, rows, and horizons
Domain
AI/ML, Probabilistic Modeling, Stochastic Dynamics
Deliverable
production ML models
Required skills
PyTorch, Transformers/attention mechanisms, probabilistic modeling in neural networks, probability and statistics, GPU-native implementation, experimental design
Preferred skills
Stochastic differential equations, synthetic data generation, quantitative domain expertise (finance/energy), mixed-modality deep learning
Responsibilities
Designing core model architecture (encoders, attention, output heads), running controlled architecture studies, building scalable PyTorch implementations, extending synthetic-data engines, turning research ideas into robust implementations