Deep Learning Research Engineer Intern

What you'd do

• 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, including the tradeoffs between quality, memory, and latency.
• Experience with probabilistic modeling in neural networks: distributional output heads, likelihood-based or proper-scoring-rule losses, mixture or flow models, or related uncertainty-aware learning setups.
• Strong foundation in probability and statistics, enough to reason about densities and masses, dependence between variables, and calibration.
• Ability to connect architectural ideas to working GPU-native implementations, controlled experiments, and diagnostics that show why a change helped.
• Strong engineering habits: readable code, tests, reproducible experiments, and disciplined evaluation of model changes.
• Ability to debug training instability, reason about why results changed, and iterate quickly from hypothesis to evidence.
• Working knowledge of stochastic processes and stochastic differential equations: drift and diffusion, jumps, regime switching, mean reversion, heavy tails, and how such dynamics are simulated numerically.
• Experience designing synthetic data generators or simulation-based training curricula, and an understanding of how the training distribution shapes what a model learns.
• Depth in a domain with rich stochastic structure such as finance, energy, commodities, or a similarly quantitative field.