Software Engineering

Intern on the engineering team owning and shipping a real scoped project end-to-end (design to production code) in voice AI, ASR, TTS or related systems. Requires self-motivated builder with first-principles reasoning, AI-first mindset, and ability to quickly learn new languages/codebases.

About the job

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 the 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 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.