Research Staff, Data Science

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. THE OPPORTUNITY Voice is the most natural modality for human interaction with machines. However, current sequence modeling paradigms based on jointly scaling model and data cannot deliver voice AI capable of universal human interaction. The challenges are rooted in fundamental data problems posed by audio: real-world audio data is scarce and enormously diverse, spanning a vast space of voices, speaking styles, and acoustic conditions. Even if billions of hours of audio were accessible, its inherent high dimensionality creates computational and storage costs that make training and deployment prohibitively expensive at world scale. We believe that entirely new paradigms for audio AI are needed to overcome these challenges and make voice interaction accessible to everyone. THE ROLE Deepgram is currently looking for seasoned Data Scientists with demonstrated experience solving hard data problems while exploring research frontiers to join our Research Staff. Conversational audio presents incredibly rich scientific, engineering, and infrastructure challenges that are orders of magnitude harder than working with text. As a Member of the Research Staff, you will help us to build an industrial “data factory” that will be used to power the next generation of Voice AI systems - unlocking the creation of models that go beyond basic transcription and comprehension, capturing nuanced meanings in complex conversations, adapting robustly to diverse speech patterns, and generating empathic responses with human-like, contextualized speech. You will collaborate closely with our product, engineering, and data teams to build and deploy models in the most scalable voice API on the planet. We look forward to you bringing your expertise, sharing insights from your latest experiments, and collaborating with us to push the boundaries of AI and voice technology. THE CHALLENGE We are seeking Research Staff who: - See "unsolved" problems as opportunities to pioneer entirely new approaches - Can identify the one critical experiment that will validate or kill an idea in days, not months - Have the vision to scale successful proofs-of-concept 100x - Are obsessed with using AI to automate and amplify your own impact If you find yourself energized rather than daunted by these expectations—if you're already thinking about five ideas to try while reading this—you might be the researcher we need. This role demands obsession with the problems, creativity in approach, and relentless drive toward elegant, scalable solutions. The technical challenges are immense, but the potential impact is transformative.

• Build a portfolio that showcases at least one end‑to‑end voice AI project (e.g., training a speech‑to‑text model on a public dataset like LibriSpeech or Common Voice) and include metrics on latency, word‑error‑rate, and compute cost; host the code on GitHub with a clear README and a short video demo to demonstrate real‑time inference.
• Highlight hands‑on experience with the same toolchain Deepgram uses: PyTorch or TensorFlow for model development, Kaldi/ESPnet for audio preprocessing, and Docker/Kubernetes for scaling APIs; list specific libraries (e.g., torchaudio, librosa, Triton Inference Server) and include any contributions to open‑source audio‑ML projects.
• Prepare a concise research briefing (1–2 pages) of your most relevant paper or project that solves a problem Deepgram cares about—such as reducing word‑error‑rate on noisy, low‑resource domains or optimizing transformer‑based encoder‑decoder models for sub‑100 ms inference. Be ready to discuss the experimental design, ablation studies, and how the findings could be integrated into Deepgram’s production pipeline.
• During the interview, demonstrate familiarity with Deepgram’s product stack by referencing their real‑time API docs, mentioning how you would benchmark a new model against their current latency/accuracy targets, and propose concrete strategies (e.g., quantization, knowledge distillation, streaming CTC) to improve cost efficiency without sacrificing accuracy.
• When negotiating compensation, research current remote US salary bands for senior research data scientists at AI‑focused SaaS firms (typically $190‑$240 k base + 0.2‑0.4 % equity). Emphasize the value of your voice‑AI expertise and any patents or open‑source contributions, and ask for a signing bonus or additional RSU vesting to offset the higher cost of living in remote‑friendly regions.