Frontier Agents Intern (fall 2026)

Together AI Together AI · Data AI · San Francisco, CA · Research

Research intern focused on building, aligning, and scaling frontier AI agent systems for complex multi-step tasks across text and speech. Projects involve developing new training methods, curating datasets, studying failure modes, and building scalable infrastructure for agent operations.

What you'd actually do

  1. Research and implement novel techniques in one or more of our focus areas
  2. Design and conduct rigorous experiments to validate hypotheses
  3. Document findings in scientific publications and blog posts
  4. Communicate the plans, progress, and results of projects to the broader team

Skills

Required

  • Masters or Ph.D. degree in Computer Science, Electrical Engineering, Information Science, or a related field
  • Publications at leading ML, NLP, or speech conferences or journals
  • Strong knowledge of Machine Learning and Deep Learning fundamentals
  • Experience with deep learning frameworks (PyTorch, JAX, etc.)
  • Understanding of how LLMs work
  • Strong programming skills in Python
  • Familiarity with Transformer architectures and recent developments in foundation models

Nice to have

  • agentic behavior
  • human-computer interaction
  • infrastructure
  • post-training methods
  • evaluation frameworks
  • open-ended tasks
  • alignment
  • reliability
  • scalability
  • self-learning
  • long-horizon reasoning
  • curating datasets
  • non-deterministic scientific and agentic tasks
  • failure modes
  • building infrastructure
  • agent operations
  • algorithmic innovation
  • dataset and interaction design
  • systems work
  • text and speech

What the JD emphasized

  • Publications at leading ML, NLP, or speech conferences or journals
  • frontier AI systems
  • agentic behavior
  • complex, multi-step tasks
  • open-ended tasks
  • alignment
  • reliability
  • scalability
  • self-learning
  • long-horizon reasoning
  • non-deterministic scientific and agentic tasks
  • failure modes in agentic behavior
  • agent operations at scale
  • algorithmic innovation
  • dataset and interaction design
  • systems work
  • push the boundaries of what AI agents can reliably accomplish
  • frontier models
  • agentic tasks and workflows
  • frontier agents alignment
  • post-training
  • failure modes
  • safety paradigms for agentic behavior
  • new recipes (for RL or test time scaling)
  • self-learning
  • long-context tasks completion
  • agents that act on spoken input
  • complex, multi-step tasks
  • ML infrastructure that can power agent operations at scale

Other signals

  • frontier AI systems
  • agentic behavior
  • complex, multi-step tasks
  • open-ended tasks
  • algorithmic innovation