Deepgram

Backend Engineer

• Lead design and implementation of Deepgram’s backend products and services for speech AI
• Improve core inference services including networking, speech processing, audio transcoding, and latency/memory optimization
• Develop processes for measuring, building, and optimizing services to maximize system performance
• Debug complex system issues spanning networking, scheduling, and high-performance computing interactions
• Rapidly customize backend services to support customer needs
• Partner with Product to design and implement new services, features, and products end to end

Account Executive

• What You'll Do
• Self-prospecting to build a strong sales pipeline of new logos, striving to exceed quarterly and annual sales targets, as well as partnering with the Marketing and SDR teams to generate meetings
• Work closely with cross-functional teams (especially Sales Ops and Sales Engineers) to ensure sales effectiveness
• Have a depth of understanding regarding customer needs in the voice AI space, competition, and Deepgram products to be able to articulate Deepgram’s value proposition fully to highly technical buyers
• Establish and maintain relationships with key stakeholders, work with CSMs to drive upsell opportunities and manage existing accounts ongoing

Solutions Engineer

• Lead technical discovery for strategic opportunities. Map business goals to measurable technical success criteria and an evaluation plan.
• Design, scope, and execute POCs that prove value. Define datasets, metrics, and timelines that land a clear technical win.
• Build lightweight prototypes, sample integrations, and demos using Deepgram SDKs and APIs.
• Advise on architecture for accuracy, latency, scalability, and cost. Cover cloud, self-hosted, hybrid, and data residency needs.
• Run security and compliance threads common in APAC. Prepare crisp responses for Singapore PDPA, Japan APPI, Korea PIPA, Australia’s Privacy Act, and India’s DPDP Act.
• Triage and debug issues during evaluations. Collaborate with Product, Engineering, and Support to unblock customers quickly.
• Create reusable assets that scale. Turn repeated requests into working samples, reference architectures, runbooks, and field guides.
• Partner with Solutions Architects and Support for seamless continuity after initial wins. Stay involved through expansion phases when needed.
• Enable the field. Collaborate with AEs, CSMs, and partners on positioning, demos, and technical validation for priority use cases.

Senior Pre-Sales Solutions Engineer - APAC

• Lead technical discovery for strategic opportunities. Map business goals to measurable technical success criteria and an evaluation plan.
• Design, scope, and execute POCs that prove value. Define datasets, metrics, and timelines that land a clear technical win.
• Build lightweight prototypes, sample integrations, and demos using Deepgram SDKs and APIs. Show, do not tell.
• Advise on architecture for accuracy, latency, scalability, and cost. Cover cloud, self-hosted, hybrid, and data residency needs.
• Run security and compliance threads common in EMEA. Prepare crisp responses for GDPR, ISO 27001, SOC 2, and vendor risk reviews.
• Triage and debug issues during evaluations. Collaborate with Product, Engineering, and Support to unblock customers quickly.
• Create reusable assets that scale. Turn repeated requests into working samples, reference architectures, runbooks, and field guides.
• Partner with Solutions Architects and Support for seamless continuity after initial wins. Stay involved through expansion phases when needed.
• Enable the field. Collaborate with AEs, CSMs, and partners on positioning, demos, and technical validation for priority use cases.
• Remote across EMEA with periodic regional meetups.

Senior Pre-Sales Solutions Engineer - EMEA

• Lead architecture and implementation for enterprise deployments that use Deepgram speech-to-text (STT), text-to-speech (TTS), and voice agent (VA) capabilities.
• Own post-sales technical success for assigned accounts. Unblock integrations, optimize accuracy and latency, and guide customers to stable production.
• Serve as the Tier-3 escalation point for complex issues. Drive root cause analysis, propose mitigations, and write durable fixes or automation when possible.
• Design reference architectures and implementation patterns for common APAC customer use cases including contact centers, voice analytics, QA, and AI agents.
• Build high-quality samples, internal tools, or scripts that make repeatable tasks easier for customers and for our team.
• Contribute to pre-sales discovery and POCs when deep technical direction is needed. Translate business goals to a concrete plan with clear success criteria.
• Partner with Product and Engineering to prioritize fixes and features. Bring crisp customer signals that improve our roadmap.
• Contribute to knowledge systems. Capture runbooks, playbooks, and docs that scale your expertise across regions and languages.
• Participate in an on-call or incident rotation appropriate for the region and customer support tier. Help us meet or outperform regional SLAs.

Solutions Architect, Applied Engineering

Data Scientist

• 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\n• 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.\n• 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.

Research Staff, Data Science

Machine Learning Engineer

• 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.\n• 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.\n• 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.\n• 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.\n• Key Responsibilities\n 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.\n 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.\n Infrastructure & Tools: Oversee and manage training tooling, job orchestration, experiment tracking, and data storage.\n• 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.\n• If you find yourself energized... you might be the researcher we need.\n• 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.