Inferred from the employer’s wording in this job description. These role-level signals are separate from the company-wide employee-review scores below.
Voice AI Platform · San Francisco
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. There is no organization in the world that understands voice better than Deepgram.
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. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5.
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 and enormously diverse, spanning a vast space of voices, speaking styles, and acoustic conditions. Even if billions of hours of audio were accessible, its inherent high dimensionality creates computational and storage costs that make training and deployment prohibitively expensive at world scale. We believe that entirely new paradigms for audio AI are needed to overcome these challenges and make voice interaction accessible to everyone.
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. As a Member of the Research Staff, this individual should have extensive 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.
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.
Brainstorming and collaborating with other members of the Research Staff to define new LLM research initiatives
Broad surveying of literature, evaluating, classifying, and distilling current methods
Designing and carrying out experimental programs for LLMs
Driving transformer (LLM) training jobs successfully on distributed compute infrastructure and deploying new models into production
Documenting and presenting results and complex technical concepts clearly for a target audience
Staying up to date with the latest advances in deep learning and LLMs, with a particular eye towards their implications and applications within our products
Are passionate about AI and excited about working on state of the art LLM research
Have an interest in producing and applying new science to help us develop and deploy large language models
Enjoy building from the ground up and love to create new systems.
Have strong communication skills and are able to translate complex concepts clearly
Are highly analytical and enjoy delving into detailed analyses when necessary
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
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.
Brainstorming and collaborating with other members of the Research Staff to define new LLM research initiatives
Broad surveying of literature, evaluating, classifying, and distilling current methods
Designing and carrying out experimental programs for LLMs
Driving transformer (LLM) training jobs successfully on distributed compute infrastructure and deploying new models into production
Documenting and presenting results and complex technical concepts clearly for a target audience
Staying up to date with the latest advances in deep learning and LLMs, with a particular eye towards their implications and applications within our products
Are passionate about AI and excited about working on state of the art LLM research
Have an interest in producing and applying new science to help us develop and deploy large language models
Enjoy building from the ground up and love to create new systems.
Have strong communication skills and are able to translate complex concepts clearly
Are highly analytical and enjoy delving into detailed analyses when necessary
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
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
Published papers in Deep Learning Research, particularly related to LLMs and deep neural networks