Partner Success Engineer

Deepgram

1d•Hybrid

About The Position

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. Our Customer Success team, known as The Heartbeat of Deepgram, sits at the intersection of customers, product, and growth. We’re not here just to “manage accounts”; we make Deepgram successful in our partners’ environments by combining technical expertise with commercial impact and an AI-native mindset. Customer Success Engineers are hands-on partners who ensure adoption, solve complex technical challenges, and highlight opportunities for expansion, while building the systems that make our team's work compound over time. This role requires an AI-first mindset, where AI use and comfort are core to how we operate, innovate, and measure performance. Team members are expected to actively use and experiment with advanced AI tools, integrate AI into their workflows, and continuously push the boundaries of what these technologies can do. The pace of AI means change is rapid, and the day-to-day work will evolve quickly. This role is for those excited to experiment, adapt, think on their feet, and learn constantly.

Requirements

7–10+ years in Technical Account Management, Sales Engineering, and/or Enterprise Customer Success (with strong technical focus), at innovative, API-driven technology companies (developer-first, developer tools, or emerging AI/tech)
A proven track record in roles that blend customer engagement with technical expertise, navigating between solution design, technical problem-solving, and commercial growth.
Hands-on experience with running product demos, POCs, or technical workshops with enterprise customers.
Demonstrated success identifying expansions, cross-sell, and upsell opportunities in complex enterprise accounts.
Strong technical aptitude: able to discuss APIs, developer workflows, and troubleshoot basic L1-style support questions (no coding required, but must be fluent in technical conversations)
Experience engaging across technical teams (developers, architects, support) and executive buyers (CIO, CTO, VP Engineering, etc.). Demonstrated success building strong relationships and delivering key business outcomes.
Exceptional interpersonal, communication and collaboration skills, with proven ability to influence internal and external stakeholders (including executives, technical teams, and sales).
A strong sense of ownership and stewardship for your customers, and a keen focus on developing a strong personal brand.
A strong understanding of partner ecosystems, channel business models (resale, referral, integrations, co-marketing, co-selling), and multi-party commercial dynamics.
Evidence of something you've built, a tool, agent, script, dashboard, or workflow, that permanently eliminated recurring work for yourself, your team, or a partner. In your application, tell us what it was, what it replaced, and what it's still doing today. Bonus points if it scaled to serve people beyond you.
An AI-native operating model. You can point to specific workflows that structurally depend on AI and describe how you'd rebuild them if the tools you rely on disappeared tomorrow.

Nice To Haves

A background spanning solutions engineering, technical account management, or L1 support in addition to CSM or Partner Management responsibilities.
Experience working in AI, machine learning, voice AI, cloud infrastructure, or developer-first technologies.
Working fluency with automation, scripting, or agent-building (Python, TypeScript, workflow tools, agent frameworks, or equivalent). You don't need to be a software engineer, but you need to be dangerous enough to ship working systems.
Familiarity with channel/partner marketing, enablement programs, or technical enablement asset creation.

Responsibilities

Act as a technical expert and strategic advisor to strategic partners, engaging directly with developers, engineers, and executives to align business goals with technical solutions.
Build, nurture, and sustain partner and customer relationships across all personas interacting with Deepgram, from developers and program managers to CIOs, CTOs, and other C-level stakeholders.
Conduct regular on-site visits with partners, work hands-on to drive adoption, and actively embed Deepgram into their workflows.
Own and manage the full partner lifecycle: onboarding, adoption, technical enablement, expansion, and advocacy.
Focus on making Deepgram successful in the partner ecosystem, ensuring products are integrated, enabled, and adopted effectively, applying best practices, reference architectures, and creative solutions to drive outcomes.
Continuously operate in discovery mode, surfacing issues, deeply understanding their complexity and business impact, and translating them into actionable requirements for internal teams.
Confidently run live product demos, guide architecture discussions, and troubleshoot L1-style issues, bridging the gap between technical support, solutions design, and commercial growth.
Maintain a deep understanding of Deepgram APIs, integrations, and AI/ML technologies.
Proactively identify and scope opportunities for expansion (cross-sell, upsell, multi-product adoption), partnering with Sales.
Proactively initiate and lead executive business reviews, joint planning sessions, and strategy workshops to stay ahead of risks, ensure alignment, and surface opportunities for growth.
Serve as the voice of the partner internally, influencing product roadmap, GTM strategy, and the internal tools and processes we build to support partners and customers.
Lead issue resolution and serve as an escalation point for complex, cross-functional partner matters, ensuring timely and satisfactory outcomes.
Build the systems that make partner success scale. Design and ship the tools, agents, dashboards, and automated workflows that turn one-off partner interactions into repeatable, self-serve experiences.
Operate AI-first by default. Every repeatable part of your job, partner research, technical enablement prep, integration diagnostics, follow-ups, QBR analysis, should run through AI workflows you've designed. If you catch yourself doing something manually more than twice, your next move is to build the system that removes it.