Partner Success Engineer (Infrastructure)

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.

About the role

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 is expected to actively use and experiment with advanced AI tools, integrating them into everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of AI capabilities is key to success here. Candidates should be comfortable adopting new models quickly, integrating AI into workflows, and continuously pushing boundaries.

Our Customer Success team sits at the intersection of partners, product, and growth. We make Deepgram succeed inside our partners’ environments by combining deep technical expertise, commercial instinct, and an AI-native way of working. A Partner Success Engineer, Infrastructure is a hands-on customer success representative who drives joint adoption, solves hard technical problems, uncovers expansion, and owns a portfolio of strategic infrastructure partners end-to-end. This role focuses on the silicon, hardware, cloud, inference, and security platforms that Deepgram’s voice AI runs on—partners whose chips, servers, accelerators (CPU/GPU/NPU), cloud and inference platforms, and confidential-computing layers determine where and how our models can be deployed.

Responsibilities

• Serve as the technical advisor and strategic owner for a portfolio of strategic infrastructure partners, engaging everyone from developers and platform/ML engineers to CIOs and CTOs.
• Own the full partner lifecycle: onboarding, adoption, technical enablement, expansion, and advocacy.
• Drive joint adoption through live demos, workshops, deployment architecture guidance, benchmarking, troubleshooting, and best-practice recommendations.
• Lead joint technical validation: scope and run POCs and evaluations that prove Deepgram models on partner infrastructure across self-hosted, on-prem, air-gapped, dedicated, and on-device/edge deployments, including security-sensitive and regulated use cases.
• Run discovery continuously: surface partner problems, understand their business impact, and translate them into actionable requirements for product and engineering.
• Identify and scope expansion (cross-sell, upsell, multi-product, co-sell) in partnership with Sales, and activate partner channels—OEMs, distributors, marketplaces, and cloud/inference providers—to reach their customer base.
• Lead executive business reviews and joint planning sessions.
• Support joint go-to-market and co-marketing in partnership with Marketing—joint blogs, one-pagers, PR, and live demos at partner events and industry conferences—to drive awareness and activate the channel.
• Act as the voice of the partner internally—influencing roadmap (especially deployment, self-hosted, security, and edge), GTM strategy, and the tools we build to support partners.
• Track adoption, usage, health, and expansion to drive outcomes; travel to partner sites and events as needed.
• Operate AI-first by default, and build tools, agents, and workflows that eliminate recurring work for you and the broader team.

Requirements

• Significant experience in technical, customer-facing roles—TAM, sales/solutions/deployment engineering, partner or enterprise CS with a strong technical focus, implementation, or support—at API-driven, developer-first, infrastructure, or AI companies. For most people that’s roughly 7+ years, but we care more about the shape of your experience than the exact number.
• A track record that blends partner or customer ownership with technical depth: solution and deployment design, hands-on troubleshooting, and commercial growth.
• Hands-on experience running demos, POCs, or technical workshops with enterprise partners or customers—leading them, not just attending.
• Fluency discussing APIs, integrations, and developer workflows, and troubleshooting L1-style issues (no coding required, but genuinely conversant—not hand-waving).
• Working understanding of deployment and infrastructure: containers and orchestration (Docker, Kubernetes/Helm), inference on GPUs/accelerators, and the trade-offs across self-hosted, on-prem, air-gapped, dedicated, and edge/on-device deployments—including basic latency, throughput, and benchmarking concepts.
• Demonstrated success identifying and landing expansion in complex enterprise or partner accounts.
• A strong understanding of partner ecosystems and channel business models—resale, referral, integrations, co-marketing, co-selling—and multi-party commercial dynamics, ideally including hardware/silicon, cloud and inference providers, or OEM/distributor channels.
• Experience engaging both technical stakeholders (developers, platform and ML engineers, architects) and executive buyers (CIO, CTO, VP Engineering).
• Exceptional communication, influence, and relationship-building—concise and structured, across technical and business audiences.
• Something you’ve built—a tool, agent, script, or workflow—that permanently eliminated recurring work. In your application, tell us what it was, what it replaced, and what it’s still doing today.
• An AI-native operating model: specific workflows that structurally depend on AI, and a clear account of how you’d rebuild them if those tools disappeared tomorrow.

Nice to Have

• Experience in machine learning, voice AI, cloud infrastructure, or developer-first technologies.
• Familiarity with GPU/accelerator infrastructure and inference optimization—quantization, model serving, throughput/latency tuning, or benchmarking.
• Exposure to confidential computing, trusted execution environments, model/weight security, or deployments in regulated industries.
• Experience with on-device or edge AI deployment across CPU/GPU/NPU targets, model catalogs, or hardware optimization toolchains.
• Telephony / CCaaS / CPaaS background (e.g., Twilio, Genesys)—maps directly to our partner ecosystem.
• A background spanning solutions/deployment engineering, TAM, or L1 support alongside CS or partner responsibilities.
• Familiarity with channel/partner marketing, enablement programs, or technical enablement asset creation.
• 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—just dangerous enough to ship working systems.