Deepgram - Research Staff, LLMs

• 3+ years of experience in applied deep learning research, with a solid understanding toward the applications and implications of different neural network types, architectures, and loss mechanism • Proven experience working with large language models (LLMs) - including experience with data curation, distributed large-scale training, optimization of transformer architecture, and RL Learning • Strong experience coding in Python and working with Pytorch • Experience with various transformer architectures (auto-regressive, sequence-to-sequence.etc) • Experience with distributed computing and large-scale data processing • Prior experience in conducting experimental programs and using results to optimize models • IT WOULD BE GREAT IF YOU HAD • Deep understanding of transformers, causal LMs, and their underlying architecture • Understanding of distributed training and distributed inference schemes for LLMs • Familiarity with RLHF labeling and training pipelines • Up-to-date knowledge of recent LLM techniques and developments • We are seeking researchers who: • See "unsolved" problems as opportunities to pioneer entirely new approaches • Can identify the one critical experiment that will validate or kill an idea in days, not months • Have the vision to scale successful proofs-of-concept 100x • Are obsessed with using AI to automate and amplify your own impact • If you find yourself energized rather than daunted by these expectations—if you're already thinking about five ideas to try while reading this—you might be the researcher we need. This role demands obsession with the problems, creativity in approach, and relentless drive toward elegant, scalable solutions. The technical challenges are immense, but the potential impact is transformative. • Published papers in Deep Learning Research, particularly related to LLMs and deep neural networks