AI Research Internship

Lexsi Ai

Location

Remote

Employment

Internship

Experience

0-1 years

Salary

₹Not Available

About the role

Focus on topics such as library development, explainability and trust in AI, mechanistic interpretability, uncertainty and risk assessment, and reinforcement learning methodologies.

Responsibilities

Collaborate closely with our research and engineering teams on library development, explainability & trust, mechanistic interpretability, uncertainty & risk, and reinforcement learning.
Author and maintain experiment code, run systematic studies, and co-author whitepapers or conference submissions.

Full description

What You’ll Do We work on multiple frontier research ideas and challenges. If you are selected, you would be working on one of these following areas. Collaborate closely with our research and engineering teams on one of the areas: 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. General Required Qualifications 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.