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.
Operating Principles:
Deepgram is a research company that productizes as fast as possible. Four Operating Principles shape how we think, decide, and work together:
Compress before you communicate. You think it through first, then lead with the bottom line.
Resist entropy. You question what already exists before adding more process or tools.
Solve the category. You fix recurring problems at the source, not one case at a time.
Work at the frontier. You go after problems no one has solved yet.
We look for people who already work this way, and these principles shape how we grow and evaluate our team.
AI-First by Default
We build AI that millions of people talk to, and we run the company the same way. AI is how we centralize work here, and is core to how we operate.
That means more than using the tools. We expect you to:
Build, not just use. Design your own workflows and agents, and use AI to remove whole categories of work instead of doing the same work faster.
Own the output. AI can speed up the work. It doesn’t make anyone less accountable for it. You understand, verify, and stand behind everything you share, including what an agent produces for you.
Spend the time you get back at the frontier. The capacity AI creates goes toward problems no one has solved yet.
Keep pace. Models and tools change every month, and so will your work. You adopt what’s new quickly and drop what no longer earns its place.
We judge AI by what it helps you deliver and whether you stand behind the result, not by how often you use it. In interviews, expect to walk us through a workflow you built and where you drew the line between AI and your own judgment.
The Opportunity
We’re building the engine that solves customer challenges at scale. When customers hit a limit in our voice AI, you’ll measure it, fix it, and ship the fix to every customer in weeks.
Example projects:
Expanding our models to new languages and regions
Improving how text-to-speech pronounces names, terms, and numbers
Isolating a speaker’s voice from background noise and crosstalk
Giving customers early access to new voice capabilities, like steerable and cloned voices, and using their feedback to guide the research methodology
Releasing new benchmarks for Voice AI use-cases
The Role
You will thrive in this role if you enjoy:
Turning vague customer problems into clear metrics
Using whatever works to fix them, including fine-tuning, new data, and external models
Shipping work that customers use within weeks
The Challenge
We are seeking researchers who:
Own a customer problem until it measurably improves in production
Find the fastest path to a fix, even if it’s an approach no one has tried
Can tell when a result is ready for customers
If these expectations energize you, you might be the person we need. This role takes focus on the customer’s problem, creativity in approach, and a strong drive to ship.
What You’ll Do
Find where our models fall short and set targets for improvement
Design evaluations that show whether a model is better and why
Curate and generate data and fine-tune models
Build working systems quickly and ship them to production
We don’t require a specific degree. Show us hard problems you’ve solved in past projects, shipped systems, or open source work.
You May Be a Good Fit If You Have
Deep experience training, fine-tuning, and evaluating deep learning models
Strong Python and PyTorch skills
A track record of shipping ML to production
A habit of owning problems end to end and learning what you need along the way
Good judgment about which problems matter and when a result is ready to ship
Strong Candidates May Also Have
Experience with speech-to-text, text-to-speech, or voice agents
Experience with distributed training
Published papers, open source contributions, or public benchmarks
Notice: We’re aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @deepgram.com email address. If you’ve received a message claiming to be Deepgram, please forward it to careers@deepgram.com.
