Research Staff, LLMs

Requirements

• COMPANY OVERVIEW Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale.
• COMPANY OPERATING RHYTHM At Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.
• Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work.
• We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here.
• Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do.
• Additionally, we move at the pace of AI.
• However, current sequence modeling paradigms based on jointly scaling model and data cannot deliver voice AI capable of universal human interaction.
• We believe that entirely new paradigms for audio AI are needed to overcome these challenges and make voice interaction accessible to everyone.
• THE ROLE Deepgram is currently looking for an experienced researcher to who has worked extensively with Large Language Models (LLMS) and has a deep understanding of transformer architecture to join our Research Staff.
• experience working on the hard technical aspects of LLMs, such as data curation, distributed large-scale training, optimization of transformer architecture, and Reinforcement Learning (RL) training.
• THE CHALLENGE 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
• 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