Jobs at Deepgram

Own and maintain deepgram.com end-to-end (Next.js front/back), Sanity CMS, component library, CI/CD, and QA. Lead i18n, technical SEO, AI/LLM discoverability, experimentation infrastructure, marketing integrations (GTM, GA4, HubSpot), and security audits while directing agency work and shipping high-velocity marketing site changes.

Own the end-to-end product experience for AI coding agents integrating Deepgram: discovery, integration/onboarding, production use, verification, and feedback systems. Build measurement, experimentation, and optimization systems; define agent-specific developer platform requirements and prototype changes; turn agent traffic into rapid product improvements and run cross-functional execution.

As a Data Scientist at Deepgram, you'll tackle complex audio data challenges, develop scalable data pipelines, and collaborate with teams to advance voice AI technologies.

Lead end-to-end delivery of complex technical programs spanning hardware, software, and research. Define workstreams, set milestones, coordinate cross-functional teams, identify and mitigate risks, dive into technical details, and build lightweight processes and tooling to scale AI/ML infrastructure and GPU/cloud deployments.

Design and build scalable, automated evaluation pipelines and infrastructure to validate speech, audio, and multimodal models. Translate research benchmarks into enforceable pass/fail gates, run batch and streaming tests, operate canaries and continuous monitoring, integrate quality gates into CI/CD, and partner with research, training, inference, and infra teams to prevent regressions and ensure production model quality.

Own and build the full invoice-to-pay cycle, automate AP with AI, lead month-end AP close and subledger-to-GL tie-out, establish AP analytics, prepare AP for external audit, manage ERP migration and Ramp card program, and handle vendor/tax compliance and reporting.

Lead end-to-end design and execution of voice data programs: build ingestion pipelines, labeling specs, tooling, and quality safeguards; align data strategy with research and product; manage vendors and teams; track throughput, quality, and vendor performance; and prototype/deploy data tools to scale datasets that improve speech and voice AI model outcomes.

Design and implement secure, scalable backend services for voice agents, integrate AI models and telephony, improve inference, debug concurrent systems, and collaborate with Product to deliver features end-to-end.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

As a Solutions Architect at Deepgram, you'll engage with customers post-sales to resolve complex technical issues, create scalable solutions, and enhance support processes, using your engineering skills and experience in customer-focused roles.

Own and grow the self-serve developer funnel from first touch to paid customer. Define and ship product changes across onboarding, console, and activation. Build instrumentation, experimentation, and business rhythms; run rapid hypotheses, measure outcomes, and collaborate with engineering, design, dev rel, marketing, sales, and analytics.

The Strategic Partner Manager at Deepgram will build and scale technology partnerships, drive revenue motions, and operate AI-natively to enhance partner relations and ecosystem integration.

As a Pre-Sales Solutions Engineer, you will drive technical pre-sales engagements, architect custom solutions, and guide customers on API integration and deployment strategies, enhancing their experience with Voice AI technologies.

Design and implement secure, scalable backend services for speech inference and audio processing. Optimize networking, transcoding, latency, memory, and distributed compute orchestration. Measure and improve system performance, debug complex networking/scheduling/HPC issues, customize services for customers, and partner with Product to deliver features end-to-end.

Build and maintain full-stack features for Saga, Deepgram's voice OS. Develop backend services (Node.js, Socket.IO, DynamoDB, AWS Amplify), frontend interfaces (TypeScript, React, Next.js), and scalable REST/GraphQL APIs. Participate in architecture, code reviews, performance optimization, and translate product requirements into iterative deliverables.

Own technical and strategic partner relationships for infrastructure platforms (silicon, cloud, edge, confidential computing). Lead onboarding, POCs, demos, deployment guidance, troubleshooting, and joint go-to-market to drive adoption and expansion of Deepgram’s voice AI on partner hardware and platforms.

Lead corporate communications strategy and execution: own product and model launches, media and executive communications, narratives, social presence, and major company moments. Translate technical research and product advances into compelling stories for developers, enterprise buyers, partners, and analysts. Partner cross-functionally with CEO, CMO, product, brand, and demand gen to build credibility and influence in the voice AI ecosystem.

Lead Deepgram's upmarket enterprise readiness by defining strategy and sequencing for SSO, compliance, security artifacts, and Console UX. Remove procurement blockers, ship solutions across Console, APIs, and managed endpoints, and work closely with engineering, design, and sales to enable self-serve enterprise procurement and speed deal closure.

Lead and build Deepgram's AI enablement function: prototype and ship reusable agents, MCP servers, paved-road workflows, prompt and retrieval patterns, and an enablement hub. Drive company-wide AI adoption with measurable productivity metrics, embed safe-use guardrails, partner across Platform, Security, Data, and People Ops, and grow a central team and distributed champions network.

Lead end-to-end delivery of ML infrastructure and AI tooling programs, defining architecture and rollout strategies for training, serving, and monitoring. Coordinate research, engineering, product, and data teams to optimize inference cost/latency, build internal tools for experiment tracking and model versioning, and resolve bottlenecks to enable faster, scalable ML deployments.