ai deep learning phd PyTorch

ai deep learning phd PyTorch

Washington, US - Remote | Boston, MA, US | San Francisco, CA, US

required Skills:

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.

Strong Plus:

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.

Familiarity with tabular or mixed-modality deep learning: categorical, ordinal, count, and bounded targets alongside continuous ones.