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.
Requirements
- Skilled at writing high-quality and efficient code in Python and PyTorch
- Have first-author publications at leading conferences on ML or NLP (ICLR, ICML, NeurIPS, ACL, EMNLP)
- Follow the latest advances in relevant subfields of AI
- Reinforcement learning of language models
- Curation of pre-training or post-training datasets and benchmarks
- Robust evaluation of foundation models
- Running large-scale experiments on GPU clusters
Responsibilities
- Define and drive the research agenda around efficiency and performance of foundation model training
- Study recent results from the broader AI research community, analyzing their relevance to the team’s research directions and ongoing projects
- Conduct experiments to empirically validate your hypotheses and compare the outcomes with relevant related work
- Share your findings both internally and externally (e.g., at top-tier conferences on ML and NLP)
- Partner with Machine Learning Engineers to integrate advanced methods into Together’s Model Shaping platform
Other
- Can autonomously design, implement, and validate your research ideas
- Are a strong communicator, ready to both discuss your research plans with other scientists and explain them to broader audience
- Are passionate about seeing your research create real-world impact through Together AI's services and willing to work hands-on with production systems to achieve it
