Role OverviewThe role requires strong PyTorch skills and hands-on experience building and training deep models. The ideal candidate will have a solid understanding of Transformers, attention variants, and long-context or state-space sequence architectures.
What You Will Do
The candidate will be able to connect architectural ideas to working GPU-native implementations, controlled experiments, and diagnostics that show why a change helped.
Why It Might Be a Fit
The ideal candidate will have strong engineering habits, ability to debug training instability, reason about why results changed, and iterate quickly from hypothesis to evidence.
Requirements
- Strong PyTorch skills and hands-on experience building and training deep models
- Solid understanding of Transformers, attention variants, and long-context or state-space sequence architectures
- Experience with probabilistic modeling in neural networks
- Strong foundation in probability and statistics
- Ability to connect architectural ideas to working GPU-native implementations
- Strong engineering habits
- Ability to debug training instability
- Working knowledge of stochastic processes and stochastic differential equations
- Experience designing synthetic data generators or simulation-based training curricula
- Depth in a domain with rich stochastic structure
- Familiarity with tabular or mixed-modality deep learning
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