Responsibilities of the Intern:

Library Development: Architect and enhance open-source Python tooling for alignment, explainability, model alginment, uncertainty quantification, robustness, and machine unlearning
Explainability & Trust: Improve and find new observations using our and other SOTA XAI techniques (DLB, LRP, SHAP, Grad-CAM, Backtrace) across text, image, and tabular modalities to understand and present new model interpretability.
Mechanistic Interpretability: Probe internal model representations and circuits—using activation patching, feature visualization, and related methods—to diagnose failure modes and emergent behaviors.
Uncertainty & Risk: Develop, implement, and benchmark uncertainty estimation methods (Bayesian approaches, ensembles, test-time augmentation) alongside robustness metrics for foundation models.
Tabular Foundational Models (Orion): Work with our leading Tabular Foundational Model team to improve and launch new tabular foundational model architectures and work on our leading opesource library TabTune.
Reinforcement Learning: Explore new ideas and algorithm around RL and our new RL fine-tuning library.
Research Contributions: Author and maintain experiment code, run systematic studies, and co-author whitepapers or conference submissions.

Requirements:

Strong Python expertise: writing clean, modular, and testable code.
Theoretical foundations: deep understanding of machine learning and deep learning principles with hands-on experience with PyTorch.
Transformer architectures & fundamentals: comprehensive knowledge of attention mechanisms, positional encodings, tokenization and training objectives in BERT, GPT, LLaMA, T5, MOE, Mamba, etc.
Version control & CI/CD: Git workflows, packaging, documentation, and collaborative development practices.
Collaborative mindset: excellent communication, peer code reviews, and agile teamwork.