Company Overview
Deepgram is bringing the world’s most natural interface, voice, to the technology we use every day. Our speech models and real-time infrastructure power AI systems that keep up with the way people actually speak. More than 5,000 organizations run conversational voice AI on Deepgram in production, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Cresta, Granola, and Jack in the Box.
The Deepgram Voice Agent Platform brings together speech-to-text, text-to-speech and speech-to-speech, with voice-native foundation models available through cloud APIs or self-hosted and on-premises deployments. Built for accuracy, low latency and cost efficiency, Deepgram makes high-quality voice experiences practical to build and run at scale. More than 650,000 developers have used Deepgram to process more than 200,000 years of audio. Every day, more of the world’s conversations with AI run on our models.
AI-First by Default
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. If you’re energized by experimenting, adapting, and learning constantly, you’ll thrive here. Expect your day-to-day to evolve as fast as the technology does.
Team Overview
The Applied AI team takes on the voice AI problems that don’t have off-the-shelf answers. We work directly with enterprise customers and technology partners, from conversational AI and customer experience platforms to restaurant brands, and much of the work is zero to one. Current focus areas include robust ASR in noisy outdoor environments, self-service fine-tuning and model adaptation, domain-aware voice agents for drive-thru and phone ordering, agent scoring and evaluation, real-time operational analytics, and on-site hardware integration. We’re a small team that works in person in San Francisco, ships to production quickly, and stays close to the customers we build for.
Key Goals
Ship production features in live voice agent products, such as drive-thru and phone ordering, within your first quarter.
Own customer problems end to end: hear the issue, find the root cause, and get the fix into production.
Help build reusable agent modules, like ordering and reservations, so new customers and industries launch faster.
Make agents more accurate on real-world menus and catalogs, keeping structured data consistent across hundreds of locations.
Contribute to fine-tuning and model adaptation so customers get accurate results on their own vocabulary and domains.
Spot problems across the product before anyone asks, then fix them or bring a proposed solution.
Run experiments that show whether new functionality improves outcomes for customers.
Minimum Skills, Knowledge & Capabilities
Experience building and shipping backend services that run in production in the cloud.
Operates AI-native: you build with modern AI coding tools as your default way of working, not occasionally, and use them to move fast without lowering the quality bar.
Learns quickly and takes on unfamiliar parts of the stack with little ramp-up.
Finds work instead of waiting for it: you recognize problems, propose solutions, and follow them through.
Has taken something from idea to production, at work or on your own time.
Communicates clearly with teammates and is comfortable working directly with customers.
Builds for the customer’s outcome, with a high bar for quality and no over-engineering.
Comfortable working across languages. Our stack is mainly Kotlin and Python, with TypeScript on the front end.
Preferred Qualifications (nice-to-have)
Hands-on experience training, fine-tuning, or adapting models, through work, research, or substantial projects.
Experience building evaluation or scoring for models or agents.
Experience with audio, speech-to-text, or text-to-speech.
Experience as a founder or early startup engineer, or taking a system from zero to one.
Typically 2+ years of engineering experience, or equivalent demonstrated experience.
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