研究资深员工，大语言模型
Research Staff, LLMs
## **公司简介**
Deepgram 是支撑新兴万亿美元语音 AI 经济的领先平台，提供实时 API 用于语音转文本（STT）、文本转语音（TTS），并可大规模构建生产级语音代理。超过 20 万名开发者和 1300 多家机构使用“由 Deepgram 提供支持”的语音服务，包括 Twilio、Cloudflare、Sierra、Decagon、Vapi、Daily、Cresta、Granola 和 Jack in the Box。Deepgram 的语音原生基础模型通过云 API 或自托管及本地软件访问，具有无与伦比的准确性、低延迟和成本效率。由领先的全球投资者和战略合作伙伴主导的最新 C 轮融资支持，Deepgram 已处理超过 5 万年的音频，并转录了超过 1 万亿个单词。世界上没有哪家公司比 Deepgram 更了解语音。
## **公司运营节奏**
在 Deepgram，我们期望具备 AI 首先的思维模式——AI 的使用和熟悉度不是可选的，而是我们运作、创新和衡量绩效的核心。
每一位在 Deepgram 工作的成员都应积极使用和尝试先进的 AI 工具，甚至将你自己的工具融入日常工作中。我们衡量 AI 应用的有效性以实现结果，持续且富有创意地使用最新 AI 能力是这里成功的关键。候选人应能够快速采用新模型和新模式，将 AI 整合到工作流程中，并不断推动这些技术的边界。
此外，我们以 AI 的速度前进。变化迅速，你可以期待你的日常工作同样快速演变。如果你对实验、适应、临场思考和不断学习不感到兴奋，或者你寻求的是高度指导性的传统 9 到 5 工作，那么这可能不是适合你的角色。
### 机会
语音是人机交互最自然的媒介。然而，基于模型和数据联合扩展的当前序列建模范式无法实现能够实现普遍人类交互的语音 AI。这些挑战源于音频带来的基本数据问题：真实世界的音频数据稀缺且极其多样化，涵盖了广泛的语音、说话风格和声学条件。即使可以获取数十亿小时的音频，其固有的高维度性也会产生计算和存储成本，使训练和部署变得过于昂贵。
查看英文原文
## **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. There is no organization in the world that understands voice better than Deepgram.
## **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. 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.
### 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 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.
### 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. 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.
### 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 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.
### What You'll Do
- 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
### You'll Love This Role if You
- 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
### It's Important to Us That You Have
- 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
### 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 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.
### What You'll Do
- 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
### You'll Love This Role if You
- 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
### It's Important to Us That You Have
- 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
- 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
_**Notice**: We're aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @_ [_deepgram.com_](http://deepgram.com) _email address. If you've received a message claiming to be Deepgram, please forward it to careers@deepgram.com._