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.

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.

Deepgram is looking for a Senior Data Scientist to sit at the intersection between research and data: people who think deeply about what conversational data is actually composed of, what makes data valuable, and how to best leverage that.

What You'll Own

• Understand and characterize our data: Build the analysis that tells us what we actually have — languages, conditions, domains, speakers, quality — and what's underrepresented.
• Design and build active-learning loops: Decide what's worth working on next based on where it will move performance, and make that decision systematic rather than intuitive.
• Think deeply about how to best leverage humans in the loop: Human attention is the scarcest input in this system. Design the workflows, tooling, and model-assisted steps that make it count.
• Make representative benchmarking possible: Build the curated datasets and methodology that let us make honest claims about model quality across the full diversity of real-world speech.
• Change our minds about what data strategies actually work: Run the experiments that separate what works from what everyone assumes works.
• Care about data consistency, cleanliness, and organization: Make data legible and accessible to non-technical teams, not just to the people who built the pipelines.
• Bring method and automation to model adaptation: Turn one-off, domain- and customer-specific model work into repeatable, documented pipelines.

What We're Looking For

• Hands-on work on real data pipelines and model-facing problems in data science, ML, or applied research.
• Strong Python and data tooling; comfort building analysis, scoring, and automation yourself.
• Experience with data characterization, data selection, active learning, or similar "what should we work on next" problems.
• Working familiarity with speech/audio or NLP models — you can reason about model output quality, confidence, and error modes.
• A track record of turning ambiguous, messy data situations into measurable model or product improvements.
• You build systems others run without you in the room — a reusable harness, not a one-off notebook.
• Strong communication skills, especially translating complex findings for audiences who don't share your background.
• An active AI-tool user.

Nice-to-haves:

• Direct experience with ASR/TTS, audio data, or multilingual/code-switched data.
• Experience with ensemble labeling, pseudo-labeling, or LLM-assisted annotation.
• Familiarity with data provenance, PII/GDPR-aware pipelines, or model-improvement compliance.
• Experience building custom or fine-tuned models for specific customers or domains.
• Comfort working directly with research and engineering teams on shared infrastructure.