Research Staff, LLMs - Deepgram

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. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words.

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 is expected to actively use and experiment with advanced AI tools, integrate AI into their workflows, and continuously push the boundaries of what these technologies can do.

The Opportunity

Voice is the most natural modality for human interaction with machines. However, current sequence modeling paradigms based on jointly scaling model and data cannot deliver voice AI capable of universal human interaction. The challenges are rooted in fundamental data problems posed by audio: real-world audio data is scarce, diverse, and high dimensional. 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 who has worked extensively with Large Language Models (LLMs) and has a deep understanding of transformer architecture to join our Research Staff. As a Member of the Research Staff, you will work on the hard technical aspects of LLMs, such as data curation, distributed large-scale training, optimization of transformer architecture, and Reinforcement Learning (RL) training.

What You'll Do

• Brainstorm and collaborate with other members of the Research Staff to define new LLM research initiatives
• Conduct broad literature surveys, evaluating, classifying, and distilling current methods
• Design and carry out experimental programs for LLMs
• Drive transformer (LLM) training jobs successfully on distributed compute infrastructure and deploy new models into production
• Document and present results and complex technical concepts clearly for a target audience
• Stay up to date with the latest advances in deep learning and LLMs, with a particular eye towards their implications and applications within our products

What We're Looking For

• 3+ years of experience in applied deep learning research, with a solid understanding of the applications and implications of different neural network types, architectures, and loss mechanisms
• Proven experience working with large language models (LLMs) - including 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

Nice to Have

• 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
• Published papers in Deep Learning Research, particularly related to LLMs and deep neural networks