Deep Learning Research Engineer Intern

Join DataRobot as a Deep Learning Research Engineer Intern, where you'll have the opportunity to shape the architecture of a probabilistic foundation model. This role is ideal for someone at a post-doctoral level or very close to it, who is passionate about deep learning, stochastic modeling, and real-world deployment constraints. You'll work closely with a world-class team, have exposure to advanced modeling and serious implementation work, and contribute to research that aims for top-tier, state-of-the-art outcomes.

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

About DataRobot

Construire des implémentations PyTorch évolutives qui prennent en charge des espaces d'entrée et de sortie plus grands, un meilleur débit et des budgets mémoire plus serrés.

At DataRobot we encourage ALL candidates, especially women, people of color, LGBTQ+ identifying people, differently abled, and other people from marginalized groups to apply to our jobs, even if you do not check every box. We’d love to have a conversation with you and see if you might be a great fit

Experience designing synthetic data generators or simulation-based training curricula, and an understanding of how the training distribution shapes what a model learns

Responsibilities

• Contribuer à la conception et à l'amélioration de l'architecture du modèle, y compris les encodeurs d'entrée pour les données temporelles et tabulaires.
• Réaliser des études contrôlées sur l'architecture, y compris les ablations et le comportement de mise à l'échelle, et transformer les résultats en décisions de conception.
• Ability to connect architectural ideas to working GPU-native implementations, controlled experiments, and diagnostics that show why a change helped
• 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

Requirements

• Strong PyTorch skills and hands-on experience building and training deep models
• Strong foundation in probability and statistics, enough to reason about densities and masses, dependence between variables, and calibration
• Ability to debug training instability, reason about why results changed, and iterate quickly from hypothesis to evidence
• Strong engineering habits: readable code, tests, reproducible experiments, and disciplined evaluation of model changes
• Solid understanding of Transformers, attention variants, and long-context or state-space sequence architectures, including the tradeoffs between quality, memory, and latency
• 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