Deepgram
Customer Success Engineer (Scaled)
About this role
Deepgram seeks a Customer Success Engineer to drive adoption and retention across a large, pooled portfolio of customers using scalable, digital-first approaches rather than traditional 1:1 account management. You'll combine hands-on technical expertise with systems thinking to design automated journeys, self-serve resources, and strategic human touchpoints that help hundreds of developers succeed with voice AI APIs.
What you'll do
- Manage adoption, retention, and expansion across a large pooled customer portfolio using one-to-many motions
- Design and execute scalable customer journeys including onboarding sequences, automated touchpoints, and self-serve enablement
- Provide technical guidance on API integrations, architecture decisions, and troubleshooting for developer audiences
- Build and maintain reusable enablement assets such as demo scripts, integration guides, sample code, and playbooks
- Run one-to-many programs including webinars and group learning initiatives
- Identify patterns from individual customer issues and systematize solutions into scalable tools or processes
What they're looking for
- Technical troubleshooting and API integration expertise
- Systems design and process automation
- Customer journey and workflow design
- Clear written communication at scale
- Advanced AI tool proficiency and integration
- Demo and presentation skills for technical audiences
- Rapid learning and prioritization under ambiguity
- Product thinking and pattern recognition
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Deepgram
Deepgram builds voice AI and speech processing technology, offering platforms that power real-time speech recognition and audio intelligence across cloud, edge, and embedded environments. The company is hiring applied ML engineers, backend engineers, embedded AI engineers, and customer-facing roles (customer success and pre-sales engineers) to scale its AI infrastructure and drive enterprise adoption.
- Website
- deepgram.com
Likely interview questions
- Walk us through a time when you took a one-off customer fix and turned it into a scalable solution—what was the problem, and how did you systematize it?
- Tell us about your current AI workflow. How have you rebuilt your work around AI tools, and what would it feel like to go back to the old way?