Research Engineer, Post-Training
## **公司概述** Deepgram 是支持新兴数万亿美元的语音人工智能经济的领先平台，为语音到文字 (STT),文本到语言 (TTS) 提供实时 API,并建立规模级生产级语音代理。超过200,000名开发人员和1,300多个组织构建由 Deepgram 发动的语音产品，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 或作为自主托管和本地软件访问，具有无与伦比的准确性，低延迟率和效率。支持由全球领先投资者和战略合作伙伴领导的 C 系列，Deepgram 处理了超过50,000年的音频并转录了超过1万亿字。世界上没有组织比 Deepgram 更好地理解声音.## **公司运营节奏** 在 Deepgram,我们预计人工智能优先思维方式 AI 使用和舒适性不是可选，它们是我们如何操作，创新和测量性能的核心。在 Deepgram 工作的每个团队成员都应该积极使用和实验高级人工智能工具，甚至构建自己的日常工作中。我们测量人工智能如何有效地应用到结果，持续的创造性使用最新的人工智能能力是成功的关键。申请人应该能够快速采用新模式和模式，将人工智能集成到他们的工作流程中，并不断推进这些技术的界限。另外，我们随着人工智能的发展而进步。变化是快速的，你可以预计你的日常工作也会迅速地演变成。如果你不愿意实验，适应，立足思考和不断学习，或者如果你正在寻找具有强制性的东西，则这可能不是正确的角色。传统的9-5###**机会。我们正在构建一个可以解决客户挑战的引擎。当客户在我们的语音人工智能中达到极限时，你会测量它，修复它，并将修正运送给每个客户几周内。示例项目：- 将我们的模型扩展到新语言和地区 - 改善文字对语音发音方式名称，术语和数字 - 从背景噪声和交叉道隔离扬声器声音 - 让客户早期获取新的语音功能，如可控制和克隆的声音，并利用他们的反来指导研究方法 - 发布新的语音使用标准##
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## **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**
We're building the engine that solves customer challenges at scale. When customers hit a limit in our voice AI, you'll measure it, fix it, and ship the fix to every customer in weeks.
Example projects:
- Expanding our models to new languages and regions
- Improving how text-to-speech pronounces names, terms, and numbers
- Isolating a speaker's voice from background noise and crosstalk
- Giving customers early access to new voice capabilities, like steerable and cloned voices, and using their feedback to guide the research methodology
- Releasing new benchmarks for Voice AI use-cases
### **The Role**
You will thrive in this role if you enjoy:
- Turning vague customer problems into clear metrics
- Using whatever works to fix them, including fine-tuning, new data, and external models
- Shipping work that customers use within weeks
### **The Challenge**
We are seeking researchers who:
- Own a customer problem until it measurably improves in production
- Find the fastest path to a fix, even if it's an approach no one has tried
- Can tell when a result is ready for customers
If these expectations energize you, you might be the person we need. This role takes focus on the customer's problem, creativity in approach, and a strong drive to ship.
### **What You'll Do**
- Find where our models fall short and set targets for improvement
- Design evaluations that show whether a model is better and why
- Curate and generate data and fine-tune models
- Build working systems quickly and ship them to production
We don't require a specific degree. Show us hard problems you've solved in past projects, shipped systems, or open source work.
### **You May Be a Good Fit If You Have**
- Deep experience training, fine-tuning, and evaluating deep learning models
- Strong Python and PyTorch skills
- A track record of shipping ML to production
- A habit of owning problems end to end and learning what you need along the way
- Good judgment about which problems matter and when a result is ready to ship
### **Strong Candidates May Also Have**
- Experience with speech-to-text, text-to-speech, or voice agents
- Experience with distributed training
- Published papers, open source contributions, or public benchmarks
_**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._