ai deep learning phd PyTorch
Summary
Machine learning researcher/engineer role (remote within the US, with hubs in Washington, Boston, and San Francisco) building and training deep models in PyTorch, focused on probabilistic, uncertainty-aware architectures, Transformers and sequence models, GPU-native implementations, and rigorous, reproducible experimentation.
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.
