Research Intern (Model Shaping, Winter 2027)

Together Computer Inc; Amsterdam; On-site; EUR 17,000 - 23,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

401(k) plan

Commuting stipend

Job summary

Together AI's Model Shaping team in Amsterdam invites a Research Intern to work on advanced post-training methods, distributed training techniques, and robust evaluation of foundation models. You will design and run experiments across supervised learning, reinforcement learning, and preference optimization, and contribute to scientific publications.

The 12 to 14 week internship spans January to April, offering collaboration with engineers and researchers across multiple teams and opportunities

Qualifications

• Familiarity with Transformer architecture and foundation models developments.
• Strong knowledge of ML and DL fundamentals.
• Currently pursuing a Bachelor’s, Master’s, or Ph.D. in CS, EE, or related field.
• Experience with deep learning frameworks (PyTorch, JAX, etc.).
• Prior research in training foundation models or efficient ML.
• Publications in leading ML/NLP conferences (NeurIPS, ICML, ICLR, ACL, EMNLP).
• Contributions to open-source ML projects.
• Understanding of model optimization techniques and hardware acceleration approaches.

Responsibilities

• Design and conduct rigorous experiments to validate hypotheses.
• Research and implement novel techniques in focus areas.
• Document findings in scientific publications and blogs.
• Integrate results into Together products.

Skills

Transformer familiarity

ML & DL fundamentals

Pursuing degree (BSc/MSc/PhD) in CS/EE

Deep learning frameworks experience

Foundation models research

Publications in ML/NLP venues

Open-source contributions

Model optimization concepts

Education

Bachelors/Masters/PhD in CS or EE

Tools

PyTorch

JAX

Job description

• 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)


• 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


• 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 January 4th to April 9th either in our San Francisco or Amsterdam Office

Benefits

• Competitive health insurance plans


• Dental and vision insurance


• Pre-tax flexible spending accounts


• Income protection & retirement


• Mental health support and services


• AD&D insurance


• Life insurance


• STD & LTD insurance


• 401(k) plan


• Monthly commuting stipend + pre-tax bene


• Team-driven celebrations and events


• Flexible time off policy


• Monthly team lunches

• Familiarity with the Transformer architecture and recent developments in foundation models


• Strong knowledge of Machine Learning and Deep Learning fundamentals


• Currently pursuing a Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field


• Experience with deep learning frameworks (PyTorch, JAX, etc.)


• 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)


• Contributions to open-source machine learning projects


• Understanding of model optimization techniques and hardware acceleration approaches