Together AI
Frontier Agents Intern (Fall 2026)
San Francisco · Intern · Internship
Sponsorship not specified$12k-$16kDetected 28 days ago
PythonAlgorithmsMachine LearningDeep LearningPyTorchNLPLLMsAgentic AIElectrical EngineeringResearch
About the role
- As a research intern, you will work on problems at the frontier of agentic AI, where challenges in alignment, reliability, and scalability are deeply intertwined.
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
- Training, developing, and evaluating frontier models, especially in the domain of agentic tasks and workflows
Requirements
- Currently pursuing a Masters or Ph.D. degree in Computer Science, Electrical Engineering, Information Science, or a related field
- Familiarity with Transformer architectures and recent developments in foundation models
Compensation
- We offer competitive compensation, housing stipends, and other competitive benefits.
- The estimated US hourly rate for this role is $58 - $63 an hour.
- Our hourly rates are determined by location, level and role.
- Individual compensation will be determined by experience, skills, and job-related knowledge.
Benefits
- Strong knowledge of Machine Learning and Deep Learning fundamentals
- Experience with deep learning frameworks (PyTorch, JAX, etc.)
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.
Visa & Work Authorization
- t 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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This listing is sourced directly from Together AI's careers page and normalized into a canonical job model.