Research Scientist, Post-Training

Together AI • San Francisco, CA

159d • $225,000 - $300,000 • Remote

About The Position

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. As a Research Scientist in Post-Training, you will advance the methods for making foundation models more useful and reliable. You will analyze the limitations of current approaches to post-training across domains such as data curation, reinforcement learning, and model evaluation. Based on this analysis, you will design new techniques for model adaptation, as well as benchmarks for measuring the progress in the field. After evaluating your ideas through experimentation, you will present your findings to the global scientific community at leading ML/NLP conferences and collaborate with your teammates to integrate those improvements into Together's platform.

Requirements

• Can autonomously design, implement, and validate your research ideas
• 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)
• Are a strong communicator, ready to both discuss your research plans with other scientists and explain them to broader audience
• Follow the latest advances in relevant subfields of AI
• 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

Nice To Haves

• 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

Benefits

• Competitive compensation
• Startup equity
• Health insurance
• Flexibility in terms of remote work

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree
• Number of Employees: 101-250 employees