Research Intern, Model Shaping (Winter 2027)

Together AI is hiring a Research Intern, Model Shaping (Winter 2027) in San Francisco, United States. It pays $58-$70/hr and Level rates it ; you can apply on Together AI.

Level's read on this job

Level rates this role Builds AI ●●●●: the job is building AI systems.
The posting mentions PyTorch, JAX and Cursor among its tools and skills.
Level classes it as an internship.
Together AI has 87 other open roles on Level.

AI in this role

pytorchjaxcursorelevenlabsdecagon

fine-tuningnlp

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:

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 January 4th to April 9th either 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.

Please see our privacy policy at https://www.together.ai/privacy

How we rate this

Research Intern, Model Shaping (Winter 2027) at Together AI rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Research Intern, Model Shaping (Winter 2027) is an open job at Together AI.
Level rates this role AI Level 4 of 4: Builds AI.
The listed pay is $58-$70/hr.
The role is based in San Francisco, Amsterdam.
The job was posted on September 18, 2026.
Seniority: intern.
Applications are handled by Together AI. Apply on Together AI

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningNLPPyTorchJAXCursorElevenlabsDecagon

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
What NLP problem have you worked on, and how did you measure whether it actually worked?
What are the limits of PyTorch that you've run into, and how did you work around them?
What's a project where you used JAX hands-on?
Walk me through how you've used Cursor in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Fine-tuning, NLP, PyTorch, JAX, and Cursor. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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