Partner Success Engineer (Infrastructure)

You’re not a traditional CSM, Technical Account Manager or Partner Manager. You operate at the intersection of three competencies, and you’re genuinely strong across all three:

Expert technical consulting — you run demos, guide deployment architecture discussions, troubleshoot integrations, and help partners and their customers stand up Deepgram across self-hosted, on-prem, dedicated, and edge environments. No coding required, but you’re fluent in APIs, containers and orchestration, inference on GPUs/accelerators, and real technical conversations.

Strategic partner management — you build trust from individual developers and platform engineers up to CTOs, own the full partner lifecycle, and turn technical adoption into channel growth across OEMs, distributors, cloud and inference providers, and other multi-party commercial relationships.

AI-native operating model — AI is how you work, not a tool you occasionally reach for. When you hit recurring work, your instinct is to build the system that removes it.

You may have been a Partner Manager, Channel Manager, Technical Account Manager, Solutions, Deployment, or Sales Engineer, Implementation or Infrastructure Engineer, strategic CSM, or Support Engineer — ideally with exposure to infrastructure, platform, or hardware ecosystems. Whatever your path, you’re probably strongest in one or two of these competencies — but you can demonstrate all three, and you’re eager for a role where you deploy them concurrently.

You thrive on bringing definition to ambiguity. You form a point of view and bring a recommendation rather than staying in open-ended discovery mode. You’re comfortable operating outside your comfort zone, you question the status quo, and you learn fast.

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 — making Deepgram successful inside the partner’s environment and on the partner’s hardware and platforms.

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. Your impact is measured by the leverage you create, not just the partners you serve.

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.

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.

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