Job Description

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.

Team Overview

You'd join the engineering team, which builds the voice-native foundation models and the platform that delivers them at production scale: real-time ASR, next-generation TTS, and LLM connectivity. As an intern, you won't be sidelined on throwaway work — you'll own a real, scoped project with a dedicated mentor, ship code that customers or teammates actually use, and get a close-up view of how research, engineering, and customers form one feedback loop.

Key Goals:

• Own and ship one scoped project end-to-end over the internship — from design to reviewed, tested code running in [staging/production] — with mentor check-ins at each milestone.

• Land merged pull requests that improve [a component or product pillar — e.g., the ASR pipeline, TTS latency, or the developer SDKs].

• Make one measurable improvement that outlasts your term.

• Use AI as a default part of how you build — [e.g., to prototype, test, or debug faster] — and share one workflow improvement back with the team.

• Ramp on Deepgram's codebase and [your domain — e.g., speech/audio ML or real-time systems] deeply enough to debug and extend [system] with decreasing hand-holding by the end of the term.

• Present your project and learnings to the team, and leave behind documentation so the next person builds on what you started.

Minimum Skills, Knowledge & Capabilities:

• You've built things because you wanted them to exist — projects, tools, scripts, or automations, whether in class, on your own, or in a prior role.

• You reach for AI as a default part of how you learn and build, not an occasional add-on — and you can talk about where it helps and where human judgment still has to lead.

• You reason from first principles: when something breaks, you dig into why rather than patching around it.

• You write and read code in at least one language, and you pick up new languages, tools, and codebases quickly.

• You can explain your work clearly — what you built, what broke, and what you'd do differently.

• You treat "good enough" as a question, not a finish line, and you're drawn to hard problems.

• You give and receive feedback well and want to get better fast.

Preferred Qualifications

• Currently pursuing a degree in computer science, engineering, or a related field — or building equivalent skills through self-study, open source, or your own projects.

• Coursework or hands-on exposure to [a relevant area — e.g., machine learning, distributed systems, audio/speech processing, or backend/web development].

• Have built or contributed to a project involving [AI/ML, real-time systems, or APIs] — hackathons, coursework, and personal projects all count.

• A prior internship or a project where you shipped something real, or an AI-assisted workflow you built for yourself.

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