Foundation-Model Research Intern — Deep Learning

DataRobot; United States; Remote; USD 55,000 - 75,000; Full time

Job summary

DataRobot seeks a research-engineering intern at post-doctoral level to shape a probabilistic foundation model. You will implement and evaluate encoders reading temporal, tabular, and mixed-modality data, with attention and decoding schemes in a production-quality PyTorch codebase.

You bring strong PyTorch skills, experience with probabilistic modeling, and a track record of rigorous experiments. A fast, results-driven team offers a hands-on environment with real impact on model development and

Qualifications

• Post-doctoral level research experience or close to it.
• Strong PyTorch and deep learning background.
• Experience with probabilistic modeling and uncertainty-aware learning.
• Ability to implement and evaluate PyTorch models in production-quality code.

Responsibilities

• Design core model architecture for temporal, unordered tabular, and mixed-modality data.
• Run architecture studies and translate results into design decisions.
• Build scalable PyTorch implementations for larger input/output spaces.
• Study interactions between data generation and inference constraints.

Skills

Deep learning

Research mindset

Experiment design

Diagnostics for model changes

Debug training instability

Engineering discipline

Tools

PyTorch

Transformers

Python

Job description

DataRobot seeks a research-engineering intern at post-doctoral level to shape a probabilistic foundation model. You will implement and evaluate encoders reading temporal, tabular, and mixed-modality data, with attention and decoding schemes in a production-quality PyTorch codebase.

You bring strong PyTorch skills, experience with probabilistic modeling, and a track record of rigorous experiments. A fast, results-driven team offers a hands-on environment with real impact on model development and