The Opportunity:This is an early hire in the Deepgram For Restaurants business and the person will start off owning in-market customer deployments. You will lead end-to-end execution of our AI implementations - connecting dots and solving problems between the nexus of the customer (both corporate and in-store employees, and consumers) as well as the Deepgram for Restaurants product/software team. The mission of the job is to make the product work like magic at each customer deployment, build momentum to expand within each customer and open pathways into new logos.
What You'll Do- Deeply understand our customers business - even better than they do - and be an expert on Deepgram's product offerings.
- Partner with our Sales, Product, and Engineering to scope, build, and ship AI agents that handle thousands of customer interactions a day.
- Serve as the primary point of contact for large enterprise stakeholders and ensure flawless delivery of complex AI solutions across enterprise deployments
- Monitor performance, adoption, and feedback from live deployments
- Translate field insights into actionable product requirements for eng/AI teams
- Help define and refine ROI metrics that drive large expansion deals
It's Important To Us That You Have- Strong customer-facing instincts: can manage corporate relationships and can anticipate and avoid issues, deftly de-escalate problems, and build confidence and evangelical support from customer decision makers
- Deep curiosity and operational empathy - loves to spend time in-store where the magic happens and you think of yourself as an AI user anthropologist
- Although this is not explicitly a product management role, you have a product management 4vibe4 and any engineer interfacing with you would think you are a product manager
- High slope: Learns fast, goes beyond role boundaries, thrives in ambiguity, play maker orientation, focus on doing what is right, intense need to win
It Would Be Great if You Had- Experience in QSR, retail, or field operations.
- Previous early-stage startup experience.
- Exposure to data labeling, product ops, or implementation at tech co9s.
- Consulting or entrepreneurial background (e.g., ex-BCG or former founder).
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