Research Intern, Model Shaping (Summer 2027)

Role Overview

As a Research Intern in the Model Shaping team, you will work on one or more of the following areas:

• Advanced post-training methods across supervised learning, preference optimization, and reinforcement learning
• New techniques and systems for efficient training of neural networks (e.g., distributed training, algorithmic improvements, optimization methods)
• Robust and reliable evaluation of foundation model capabilities

The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition to that, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems.

Past research led by Model Shaping interns resulted in the following publications:

• Escaping the Verifier: Learning to Reason via Demonstrations (ICML 2026)
• Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking (ICML 2026)
• FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models (ICLR 2026)

Responsibilities

• Research and implement novel techniques in one or more of our focus areas
• Design and conduct rigorous experiments to validate hypotheses
• Document findings in scientific publications and blog posts
• Integrate the research results into Together products

Requirements

• Currently pursuing a Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field
• Strong knowledge of Machine Learning and Deep Learning fundamentals
• Experience with deep learning frameworks (PyTorch, JAX, etc.)
• Familiarity with the Transformer architecture and recent developments in foundation models

Preferred Requirements

• Prior research experience with training foundation models or efficient machine learning
• Publications at leading ML and NLP conferences (such as NeurIPS, ICML, ICLR, ACL, or EMNLP)
• Understanding of model optimization techniques and hardware acceleration approaches
• Contributions to open-source machine learning projects