Research Intern, Model Shaping (Summer 2027)

About the Role 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 About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers.

AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own.

Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Internship Program Details Our internship program runs 12 to 14 weeks, giving you the opportunity to work alongside industry-leading engineers and researchers across multiple teams.

This cohort's internship dates span either May 17th to August 6th or June 14th to September 3rd in our San Francisco or Amsterdam office. Compensation We offer competitive compensation, housing stipends, and other competitive benefits.

The estimated US hourly rate for this role is $58 to $70. Our hourly rates are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Application Policy Please read each role carefully and apply to the position best suited to you. Candidates are limited to 6 applications within a 90-day period, and applications beyond that limit will be automatically rejected.

You may apply to additional roles 90 days after your last application. 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.

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