[Hiring] Software Engineering Internship @Deepgram

Role Description

As an intern at Deepgram, you will join the engineering team, which builds the voice-native foundation models and the platform that delivers them at production scale. 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.

• 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.

Qualifications

• 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.

Requirements

• 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.