Audio Engineer

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.

OPPORTUNITY:

Deepgram is looking for an Audio Engineer to own and scale audio quality across our voice AI products. This role sits at the intersection of professional audio engineering and machine-learning infrastructure. You will be responsible for ensuring our voices don’t just sound “correct,” but sound genuinely great to human listeners, across thousands of voices, recording conditions, and use cases.

This is a foundational role. You’ll help define how audio engineering fits into our end-to-end pipeline: from on-site voice actor recording, to speaker-specific cleanup for fine-tuning, to synthetic data generation, and large-scale TTS training. You’ll take traditionally manual, GUI-driven audio workflows and turn them into scalable, programmatic systems that can operate at Deepgram’s scale.

WHAT YOU’LL DO

- Identify and correct audio artifacts, loudness inconsistencies, frequency imbalances, and sibilance issues across large-scale voice datasets.

- Design and implement scalable audio processing pipelines for voice data

- Define and implement scalable audio processing pipelines (EQ, compression, de-essing, dynamic range optimization) and normalization strategies across inter- and intra- voice recordings.

- Optimize audio quality across real and synthetic voices to ensure a consistent product experience across multiple use cases.

- Lead audio quality decisions during on-site voice actor recording sessions, including microphone selection, placement, gain staging, and environment setup.

- Define, document, and enforce audio quality standards for external vendors, including recording setup requirements, signal characteristics, and post-processing expectations, ensuring vendor-produced audio meets Deepgram’s training and product needs even when recordings are not done on-site.

- Convert expert-driven, manual audio workflows into automated, repeatable, code-based systems.

- Collaborate closely with research to improve training data quality, especially TTS speaker-specific fine-tuning.

- Contribute to synthetic data pipelines by defining and validating acoustic characteristics, guiding how different “sound profiles” should be produced and evaluated.

YOU’LL LOVE THIS ROLE IF YOU

- Instinctively hear volume, EQ, and dynamic differences that others miss.

- Obsess over why one voice sounds more pleasing than another—even when both are “natural.”

- Are equally comfortable tweaking a signal chain in Logic Pro and implementing it in FFmpeg or Python.

- Enjoy scaling handcrafted quality decisions to thousands of recordings.

- Lose sleep over missed opportunities to improve audio quality or training data diversity.

- Think ahead about how today’s recording choices enable tomorrow’s models.

IT’S IMPORTANT TO US THAT YOU HAVE

- Professional audio engineering experience (studio, podcast, radio, live sound, or equivalent).

- Deep understanding of EQ, compression, limiting, de-essing, and mastering techniques.

- Strong familiarity with professional audio tools (Adobe Audition, Logic Pro, Pro Tools, or similar).

- Hands-on experience with FFmpeg and comman