Research Intern, Model Shaping (Summer 2026)

Together AI•San Francisco, CA

About The Position

About Together AI Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancements such as FlashAttention, Mamba, FlexGen, SWARM Parallelism, Mixture of Agents, and RedPajama. 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 papers: Escaping the Verifier: Learning to Reason via Demonstrations FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models

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.)
Strong programming skills in Python
Familiarity with Transformer architectures and recent developments in foundation models

Nice To Haves

Prior research experience with 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