Job description
Company Overview
Deepgram is the leading voice AI platform for developers building speech-to-text (STT), text-to-speech (TTS) and full speech-to-speech (STS) offerings. 200,000+ developers build with Deepgram’s voice-native foundational models – accessed through APIs or as self-managed software – due to our unmatched accuracy, latency and pricing. Customers include software companies building voice products, co-sell partners working with large enterprises, and enterprises solving internal voice AI use cases. The company ended 2024 cash-flow positive with 400+ enterprise customers, 3.3x annual usage growth across the past 4 years, over 50,000 years of audio processed and over 1 trillion words transcribed. There is no organization in the world that understands voice better than Deepgram
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 seeking a highly skilled and versatile Machine Learning Engineer to join our Research Staff team. As a Member of the Research Staff, this role focuses on scaling training systems for speech related technologies, building internal tools, and driving innovation in data strategies. You’ll work at the intersection of machine learning, data infrastructure, and internal tooling to support our mission of building world-class speech recognition and synthesis systems.
Key Responsibilities
Scalable Model Training: Architect and manage horizontally scalable training systems for Speech to Text (STT) and Text to Speech (TTS) models across diverse domains, including, but not limited to: non-english languages, use cases, and customer-centric. These systems include data preparation and management, training pipelines, and automated evaluation tooling.
Tooling & Accessibility: Design and implement internal UIs and tools that make ML systems and workflows accessible to non-technical stakeholders across the company. These UIs should be designed to provide transparency and flexibility to internally built tooling.
Infrastructure & Tools: Oversee and manage training tooling, job orchestration, experiment tracking, and data storage.
The Challenge
We are seeking Members of the 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.
It’s Important to Us That You Have
Strong experience in training large-scale machine learning systems, particularly in STT or related speech domains.
Proficiency with orchestration and infrastructure tools like Kubernetes, Docker, and Prefect.
Familiarity with ML lifecycle tools such as MLflow.
Experience building internal tools or dashboards for non-technical users.
Hands-on experience with data engineering practices for unstructured audio and text data.
Comfortable working in cross-functional teams that include researchers, engineers, and product stakeholders.
Nice to Have
- Deep understanding of evaluation metrics and benchmarking techniques for ASR and/or TTS systems.
Why Join Deepgram?
At Deepgram, you’ll help shape the future of human–machine communication. Our research culture prioritizes ownership, experimentation, and real-world impact. As a Member of the Research Staff, you’ll be empowered to build tools and systems that accelerate ML research and product deployment at scale.
Backed by prominent investors including Y Combinator, Madrona, Tiger Global, Wing VC and NVIDIA, Deepgram has raised over $85 million in total funding. If you’re looking to work on cutting-edge technology and make a significant impact in the AI industry, we’d love to hear from you!
Deepgram is an equal opportunity employer. We want all voices and perspectives represented in our workforce. We are a curious bunch focused on collaboration and doing the right thing. We put our customers first, grow together and move quickly. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, veteran status, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate.
We are happy to provide accommodations for applicants who need them.
