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New York, New York, United States
Hybrid
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.
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.
Enterprise restaurant deployments are where voice AI meets the messiness of the real world: legacy systems, high transaction volumes, operational edge cases, and technology that has to work reliably during the busiest moments of the day.
In this role, you will embed with leading restaurant brands and technology partners to make those deployments successful. You will learn how their systems work, design the integration path, write code, troubleshoot production issues, and adapt Deepgram’s platform to the constraints of live restaurant operations. You will work directly with customer engineering teams and business leaders, owning the technical path from early discovery through go-live and optimization.
Your impact will extend beyond any single customer. You will identify patterns across deployments, translate field learnings into product improvements, and build the tools, reference architectures, and playbooks that help the team deploy faster and more reliably across large restaurant fleets.
This role is for an engineer who wants direct ownership of customer outcomes and enjoys turning complex, ambiguous deployments into repeatable systems. The goal is simple: make every Deepgram for Restaurants deployment feel effortless to the customer and deliver measurable value from day one.
This role is based in New York City or San Francisco, where many of our customers and partners are located. Being close to them helps us move quickly and stay connected to their needs.
This role also includes occasional travel to meet with customers, support sales engagements, and participate in key onsite interactions.
Customer engineering partnership: Embed with customer engineering teams as a trusted technical peer. Lead technical discovery and solutioning for prospects and customers.
Technical integration and deployment: Own the end-to-end technical implementation of Deepgram’s voice AI platform for enterprise customers — from scoping and architecture through go-live and optimization.
Configuration and customization: Adapt and configure Deepgram’s platform to meet each customer’s unique operational requirements — menu structures, ordering flows, POS integrations, edge cases, and everything in between.
Production excellence: Monitor, troubleshoot, and resolve technical issues in live deployments. Serve as an escalation point for complex technical issues when necessary.
Deployment infrastructure leadership: Codify patterns into tools, playbooks, reference architectures, and building blocks the broader team can use for other deployments and customers.
Feedback loop to product and engineering: Translate real-world deployment challenges into actionable requirements for Deepgram’s product and engineering teams. You are the voice of the customer inside the building.
You are hands-on. You write code, read logs, and ship fixes — you don’t just coordinate. When a customer has a technical problem, you roll up your sleeves and solve it.
You operate with extreme ownership and urgency. You treat every customer deployment as if your name is on the product.
You partner closely with Sales, Product, and Engineering, but you are just as comfortable on a customer’s Slack channel walking their team through an API integration.
Technical depth: You are an excellent software engineer. You are comfortable working across APIs, SDKs, webhooks, cloud infrastructure, and customer tech stacks. You use AI coding tools to move fast and multiply your output.
Customer obsession: Deep, personal commitment to your customers’ technical success. You take their problems home with you. You are not satisfied until the deployment is bulletproof.
High slope: Learns fast, adapts quickly, and stays effective in ambiguity without needing constant structure. You figure things out and move forward. You are excited to get your hands dirty solving gritty technical problems over sexy ones.
Communication and trust: Able to build deep credibility with both technical and non-technical stakeholders on the customer side. You are calm under pressure and relentlessly reliable.
Experience with voice AI, speech-to-text, NLP, or conversational AI systems.
Previous early-stage startup or founder-led environment experience — you thrive in environments where you wear many hats and build from zero.
Previous experience embedded within a large enterprise customer’s product deployment.
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