About the job
Deepgram stands at the forefront of the burgeoning trillion-dollar Voice AI sector, offering real-time APIs for both speech-to-text (STT) and text-to-speech (TTS) capabilities, as well as enabling the creation of scalable, production-quality voice agents. Our platform is utilized by over 200,000 developers and more than 1,300 organizations, including giants like Twilio, Cloudflare, and Jack in the Box. With exceptional accuracy, low latency, and cost efficiency, Deepgram provides voice-native foundation models that can be accessed via cloud APIs or as self-hosted and on-premises software solutions. Supported by a recent Series C funding round from top-tier global investors, Deepgram has transcribed over 1 trillion words and processed more than 50,000 years of audio, establishing itself as the premier authority in voice technology.
Company Operating Rhythm
At Deepgram, we embrace an AI-first philosophy—where comfort with AI is not just encouraged but essential for innovation and performance measurement. All team members are expected to leverage advanced AI tools regularly and integrate them into their workflows for optimal results. Rapid adaptation to new models and a commitment to pushing the frontiers of technology are crucial for success in this role. If you thrive in a fast-paced environment and relish the opportunity to experiment and learn continuously, you will find a fulfilling career here.
The Opportunity
Deepgram's speech AI models are renowned for their speed and accuracy, and we are expanding our reach to defense and edge computing sectors where our models must operate beyond traditional cloud environments. As the Defense and Edge Technology Lead, you will spearhead the technical strategy for deploying Deepgram's models in edge and embedded settings. Collaborating closely with hardware partners like Qualcomm and Motorola, you will meet the unique requirements of defense customers through AWS NatSec partnerships, while also leading efforts in model optimization and runtime engineering to ensure the delivery of production-quality speech AI in various constrained environments.

