研究工程师，机器学习系统
Research Engineer, Machine Learning Systems
## **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 服务，包括语音转文本（STT）、文本转语音（TTS），以及大规模构建生产级语音代理。超过 20 万名开发者和 1300 多家机构正在构建由 Deepgram 提供支持的语音产品，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可通过云 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领投的最新 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 优先的思维方式——AI 的使用和熟悉程度不是可选的，而是我们运作、创新和衡量绩效的核心。
在 Deepgram 工作的每位团队成员都应积极使用并尝试先进的 AI 工具，甚至将你自己的工具融入日常工作中。我们衡量 AI 应用效果以交付成果，持续且富有创意地使用最新的 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，你可以预期你的日常工作也会同样快速地演变。如果你不热衷于实验、适应、随机应变和不断学习，或者你寻求的是高度规范化的传统 9 到 5 工作，那么这可能不是适合你的职位。
### **机会介绍**
语音是人类与机器交互最自然的方式。然而，目前基于同时扩展模型和数据的序列建模范式无法实现能够进行通用人类交互的语音 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带来计算和存储成本，使训练和部署变得代价高昂。
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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**
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 seeking a highly skilled and versatile Machine Learning Engineer to join our Research team. As a Member of the Research Staff, you will partner with research scientists to prototype and validate novel modeling ideas, then scale them through robust training systems for speech technologies, internal tooling, and innovative data strategies. You'll work at the intersection of machine learning, data infrastructure, and internal tooling to support our mission of building world-class speech recognition and synthesis systems. On the Research team, you will experiment with new technologies and techniques, while also working on product-focused deliverables, learning from colleagues with a wide range of expertise in AI and machine learning as you go.
### **Key Responsibilities**
- **Scalable Model Training:** Architect and manage horizontally scalable systems that dramatically accelerate the end-to-end training lifecycle for Speech-to-Text (STT) and Text-to-Speech (TTS) models. This includes far more than automated training: the role focuses on making model development significantly faster and more efficient through optimized data preparation and management, high-throughput training pipelines, distributed infrastructure, and automated evaluation tooling.
- **Tooling & Accessibility**: Design and implement internal UIs and tools that make ML systems and workflows accessible to non-technical stakeholders across the company. These UIs should be designed to provide transparency and flexibility to internally built tooling.
- **Infrastructure & Tools**: Oversee and manage training tooling, job orchestration, experiment tracking, and data storage.
### **The Challenge**
We are seeking Members of the Research Staff 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.
### **It's Important to Us That You Have**
- Strong experience with the machine learning research pipeline, particularly in STT or related speech domains. This includes experimenting with and evaluating new architectures and modeling approaches, and implementing large-scale training systems.
- Proficiency with orchestration and infrastructure tools like Kubernetes, Docker, and Prefect.
- Familiarity with ML lifecycle tools such as MLflow.
- Experience building internal tools or dashboards for non-technical users.
- Hands-on experience with data engineering practices for unstructured audio and text data.
- Comfortable working in cross-functional teams that include researchers, engineers, and product stakeholders.
### **Why Join Deepgram?**
At Deepgram, you’ll help shape the future of human–machine communication. Our research culture prioritizes ownership, experimentation, and real-world impact. As a Member of the Research Staff, you'll be empowered to build tools and systems that accelerate ML research and product deployment at scale.
_**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._