Software Engineering- Internship (Fall 2026/Summer 2027)

Deepgram

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. Note: Hiring for Fall 2026 and Summer 2027 cohorts Team Overview You'd join the engineering team building 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 project and see firsthand how research, engineering, and customers actually fit together. Key Goals: - Design, build, and ship one scoped project end to end, from design through reviewed, tested code running in staging or production, with a dedicated mentor guiding you at each milestone - Contribute directly to a production Deepgram codebase, whether that's the core voice AI platform, the Applied AI wing (Deepgram for Restaurants), or the consumer wing, landing merged PRs that teammates and customers actually use - Dig into voice AI: speech and audio ML, real-time systems, and how research, engineering, and customers form one feedback loop - Use Agentic tooling (Claude Code, Codex, whatever you want!!) as a default part of how you prototype, test, and debug, and bring at least one workflow improvement back to the team 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 machine learning, real-time systems, or audio/speech processing. - A prior internship, a hackathon project, or something you built and shipped for yourself, ideally with an AI-assisted workflow behind it.