Probabilistic AI Research Engineer Intern

DataRobot, Inc.; Washington; Hybrid; USD 41,000 - 69,000; Full time

Benefits offered by this job

Medical

Dental & Vision Insurance

Flexible Time Off Program

Paid Holidays

Paid Parental Leave

Global Employee Assistance Program (E)

Job summary

DataRobot, Inc. is seeking a highly capable research‑engineering intern to shape the architecture of a probabilistic foundation model.

You will implement, train, and evaluate PyTorch models in production‑quality code, focusing on temporal, tabular, and mixed‑modality data with attention and sequence mechanisms. Applicants should have a strong background in stochastic modeling and a track record of building scalable deep learning systems, with emphasis on experimentation, reproducibility, and

Qualifications

• Strong PyTorch skills and hands-on experience building and training deep models.
• Solid understanding of Transformers, attention variants, and long-context architectures.
• Experience with probabilistic modeling in neural networks and uncertainty-aware learning.
• Ability to connect architectural ideas to GPU-native implementations and diagnostics.

Responsibilities

• Design core model architecture for temporal, tabular, and mixed-modality data.
• Run architecture studies from ablations to memory/throughput profiling.
• Build scalable PyTorch implementations for larger input/output spaces.
• Study how training data generation and inference constraints affect benchmark quality.

Skills

PyTorch

Transformers

Probabilistic modeling

Experimentation

Debug training

Reproducible research

Tools

PyTorch

Transformers

Job description

DataRobot, Inc. is seeking a highly capable research‑engineering intern to shape the architecture of a probabilistic foundation model.

You will implement, train, and evaluate PyTorch models in production‑quality code, focusing on temporal, tabular, and mixed‑modality data with attention and sequence mechanisms. Applicants should have a strong background in stochastic modeling and a track record of building scalable deep learning systems, with emphasis on experimentation, reproducibility, and