Research Intern, Model Shaping (Summer 2026) at Together AI

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

06 Apr, 26

Salary

63.0

Posted On

06 Jan, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Python, PyTorch, JAX, Transformer Architectures, Model Optimization, Reinforcement Learning, Preference Optimization, Neural Networks, Scientific Publications, Algorithmic Improvements, Distributed Training, Evaluation Techniques, Open Source, Data

Industry

Software Development

Description

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 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 Communicate the plans, progress, and results of projects to the broader team 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 Preferred Qualifications 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 Internship Details Duration: ~12 weeks (Summer 2026) Location: San Francisco, Amsterdam Internship Program Details Our summer internship program spans over 12 weeks where you’ll have the opportunity to work with industry-leading engineers building a cloud from the ground up and possibly contribute to influential open source projects. Our internship dates are May 18th to August 7th or June 15th to September 4th. Compensation We offer competitive compensation, housing stipends, and other competitive benefits. The estimated US hourly rate for this role is $58-63/hr. Our hourly rates are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy

Responsibilities

Research and implement novel techniques in focus areas such as advanced post-training methods and efficient training systems. Document findings and integrate research results into Together AI products.