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Staff Sales Engineer, Voice AI
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About this role.
Company Overview
Deepgram is bringing the world’s most natural interface, voice, to the technology we use every day. Our speech models and real-time infrastructure power AI systems that keep up with the way people actually speak. More than 5,000 organizations run conversational voice AI on Deepgram in production, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Cresta, Granola, and Jack in the Box.
The Deepgram Voice Agent Platform brings together speech-to-text, text-to-speech and speech-to-speech, with voice-native foundation models available through cloud APIs or self-hosted and on-premises deployments. Built for accuracy, low latency and cost efficiency, Deepgram makes high-quality voice experiences practical to build and run at scale. More than 650,000 developers have used Deepgram to process more than 200,000 years of audio. Every day, more of the world’s conversations with AI run on our models.
Operating Principles:
Deepgram is a research company that productizes as fast as possible. Four Operating Principles shape how we think, decide, and work together:
Compress before you communicate. You think it through first, then lead with the bottom line.
Resist entropy. You question what already exists before adding more process or tools.
Solve the category. You fix recurring problems at the source, not one case at a time.
Work at the frontier. You go after problems no one has solved yet.
We look for people who already work this way, and these principles shape how we grow and evaluate our team.
AI-First by Default
We build AI that millions of people talk to, and we run the company the same way. AI is how we centralize work here, and is core to how we operate.
That means more than using the tools. We expect you to:
Build, not just use. Design your own workflows and agents, and use AI to remove whole categories of work instead of doing the same work faster.
Own the output. AI can speed up the work. It doesn’t make anyone less accountable for it. You understand, verify, and stand behind everything you share, including what an agent produces for you.
Spend the time you get back at the frontier. The capacity AI creates goes toward problems no one has solved yet.
Keep pace. Models and tools change every month, and so will your work. You adopt what’s new quickly and drop what no longer earns its place.
We judge AI by what it helps you deliver and whether you stand behind the result, not by how often you use it. In interviews, expect to walk us through a workflow you built and where you drew the line between AI and your own judgment.
The Opportunity
Enterprises are moving fast from transcribing conversations to automating them. Contact centers, healthcare, financial services and consumer brands are putting voice agents in front of their customers. The buyers range from CX leaders to platform engineers, and the deals turn on latency, reliability, cost per minute and whether the agent holds up in production.
Deepgram is hiring a Staff Sales Engineer, Voice Agent to drive adoption of our Voice Agent platform globally. You’ll work alongside every Account Executive on the team as the technical lead on voice agent deals, and you’ll carry a quota for new Voice Agent logos and ARR. Most of your time is spent in front of customers: positioning Deepgram in discovery, winning the technical evaluation and getting customers to production. You report to the VP of Sales.
What You’ll Do
Win voice agent deals. Lead discovery, technical qualification, architecture and proofs of concept for Voice Agent opportunities, and own the technical win from first call through production.
Be the expert in the room. Whiteboard how a customer’s telephony stack (SIP trunks, PBX, CCaaS) connects to their voice agents and where Deepgram fits. Go deep on latency, turn-taking, interruption handling, function calling, cost per minute and build vs. buy.
Build what sells. Ship demos, reference agents and integration patterns across LLMs, telephony/SIP, CCaaS platforms and orchestration frameworks that AEs and customers can use on day one.
Carry the number. Own a quota tied to Voice Agent growth and run your pipeline and forecast with discipline.
Make the team better. Create the positioning, competitive battlecards, objection handling and enablement that let every AE sell Voice Agent with confidence, even when you are not on the call.
Bring the field into Product. Turn patterns from deals into clear, evidence-backed requirements for our API, SDKs and docs.
Work the ecosystem. Partner with CCaaS, telephony and LLM partners to build joint solutions and co-sell plays.
What You Bring
7+ years in customer-facing technical roles such as solutions engineering, sales engineering, solutions architecture or forward-deployed engineering, ideally at an API, CPaaS, CCaaS or conversational AI company.
Hands-on voice agent experience across the stack: telephony (SIP, WebRTC, PBX or CCaaS integration), orchestration (LLM agent frameworks, tool calling, real-time streaming pipelines) and models (STT, TTS, LLMs).
You build. You’ve shipped demos, POCs or production integrations in Python, Node.js or similar, and you can show us something you’ve built.
A track record in revenue-tied roles. You’ve been measured on technical wins, pipeline or quota, and you consistently hit your targets.
Comfort leading complex, multi-stakeholder enterprise deals with everyone from platform engineers to CX executives.
Clear communication. You can walk an engineer through a SIP call flow and a VP through a cost-per-minute business case in the same meeting.
Nice to Have
Voice agents built on frameworks or platforms like Pipecat, LiveKit, Vapi, Retell or OpenAI Realtime
Contact center domain experience (Genesys, NICE, Five9, Amazon Connect, Talkdesk or similar)
Experience carrying an individual or overlay quota
Hands-on work with MCP servers, agent harnesses or CLI tools, or open-source contributions with real adoption
Familiarity with Challenger or a similar sales methodology
Experience selling across both self-serve and enterprise motions
Notice: We’re aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @deepgram.com email address. If you’ve received a message claiming to be Deepgram, please forward it to careers@deepgram.com.
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Role DNA
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Core skills
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Sample interview questions
I would first clarify the business outcome, target call types, success metrics, and rollout timeline. I would then map the current telephony and contact-center architecture, integration constraints, security requirements, latency expectations, and escalation paths. Finally, I would define a measurable proof of concept with technical acceptance criteria, ownership, and a production path.
For engineers, I break latency into audio capture, network transport, speech-to-text, LLM inference, tool execution, text-to-speech, and interruption handling. For executives, I translate those components into customer experience, containment rate, call duration, and cost-per-minute impact. I recommend a target experience threshold and show which architectural choices best support it.
I would select a high-value customer workflow, such as appointment scheduling or contact-center triage, and build a modular reference implementation. It should demonstrate live speech input, turn-taking, tool calling, error handling, observability, and integration with a telephony or CCaaS endpoint. I would document setup steps and value messaging so account executives can use it consistently in customer conversations.
I would validate the appeal of control and customization, then quantify the complete build-and-operate burden. This includes model quality, latency tuning, telephony integration, reliability, monitoring, security, iteration speed, and ongoing maintenance. I would position the platform around faster time to production and lower operational risk while identifying areas where customer-specific customization remains valuable.
It means treating technical execution as a measurable part of revenue generation rather than as reactive support. I would maintain clear qualification standards, prioritize high-probability opportunities, document technical risks early, and align proof-of-concept milestones to deal stages. I would also forecast based on evidence such as executive sponsorship, technical validation, integration readiness, and a mutually agreed production plan.
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