Research Scientist, Convergence

• PhD in Computer Science, Machine Learning, or related field
• Strong publication record in top-tier ML conferences (NeurIPS, ICML, ICLR) or top journals
• Demonstrated expertise in reinforcement learning
• Deep knowledge of transformer architectures and vision-language models
• Experience training large language models using techniques such as distillation and supervised fine-tuning
• Proven track record in large-scale distributed training and inference
• Strong theoretical understanding of deep learning fundamentals
• Proficiency in PyTorch and related research frameworks

Desirable

• We are looking for Research Scientists with strong academic backgrounds to join our team and lead research initiatives on training foundation models which power Proxy, our generalist agent
• You will work with a small team of hands-on researchers — equipped with substantial GPU resources – to advance the state-of-the-art in multi-modal vision-language models, reinforcement learning, and action models
• You will develop novel architectures and training approaches while publishing your findings in top-tier AI conferences and journals
• Designing and implementing novel supervised fine-tuning, preference learning and reinforcement learning techniques
• Developing innovative methods for data curation, including synthetic data generation pipelines and optimal dataset composition strategies
• Conducting rigorous experimentation to optimize model performance through data mixes, regularization techniques, and low-level optimizations
• Advancing the theoretical understanding of transformer architectures and their applications to multi-modal learning
• Publishing research findings in top-tier venues such as NeurIPS, ICML, and ICLR