Deep Learning Research Intern: Probabilistic Foundation

DataRobot; Massachusetts; On-site; USD 55,000 - 83,000; Full time

Benefits offered by this job

Medical, Dental & Vision Insurance

Flexible Time Off Program

Paid Holidays

Paid Parental Leave

Global Employee Assistance Program

Job summary

DataRobot is seeking a research-engineering intern to help shape a probabilistic foundation model that handles temporal, tabular, and mixed-modality data. You will implement, train, and evaluate PyTorch models in production-quality code, with a focus on stochastic dynamics and synthetic data generation.

The role targets post-doctoral‑level researchers or those near that level, offering exposure to architecture studies, scalable GPU‑level implementations, and rigorous empirical validation in a

Qualifications

• 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: distributional output heads, likelihood-based or proper-scoring-rule losses.
• 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.

Responsibilities

• Design and improve the core model architecture for temporal, tabular, and mixed-modality data.
• Run controlled architecture studies, from ablations to memory and throughput profiling.
• Build scalable PyTorch implementations supporting larger input/output spaces and tighter memory budgets.
• Study how data-generation choices and inference constraints affect benchmark quality and real-world usefulness.
• Extend synthetic-data engines with richer stochastic dynamics and regime behavior.

Skills

PyTorch

Transformers

Probabilistic modeling

Uncertainty learning

Clean code

Tests

Reproducible experiments

Tools

PyTorch

Job description

DataRobot is seeking a research-engineering intern to help shape a probabilistic foundation model that handles temporal, tabular, and mixed-modality data. You will implement, train, and evaluate PyTorch models in production-quality code, with a focus on stochastic dynamics and synthetic data generation.

The role targets post-doctoral‑level researchers or those near that level, offering exposure to architecture studies, scalable GPU‑level implementations, and rigorous empirical validation in a