Probabilistic Foundation Models Intern - Research Engineer

DataRobot; San Francisco (CA); On-site; USD 90,000 - 120,000; Full time

Job summary

DataRobot seeks a post‑doctoral level research‑engineering intern to shape the architecture of a probabilistic foundation model. You will develop encoders for temporal, tabular, and mixed‑modality data, study attention mechanisms across variables, and implement, train, and evaluate PyTorch models in production‑quality code.

The role emphasizes precise reasoning about architectural capabilities, synthetic data generation, and robust experimentation to drive real‑world usefulness.

Qualifications

• Strong PyTorch skills and experience building and training deep models.
• Solid understanding of Transformers and long-context sequence architectures.
• Experience with probabilistic modeling in neural networks and uncertainty-aware learning.

Responsibilities

• Design and run architecture studies, ablations, and scaling experiments.
• Build scalable PyTorch implementations with memory and throughput in mind.
• Turn research ideas into robust implementations and credible results.

Skills

PyTorch

Transformers

Probabilistic modeling

Statistics

GPU implementation

Engineering discipline

Troubleshooting

Job description

DataRobot seeks a post‑doctoral level research‑engineering intern to shape the architecture of a probabilistic foundation model. You will develop encoders for temporal, tabular, and mixed‑modality data, study attention mechanisms across variables, and implement, train, and evaluate PyTorch models in production‑quality code.

The role emphasizes precise reasoning about architectural capabilities, synthetic data generation, and robust experimentation to drive real‑world usefulness.