Senior Technical Marketing Engineer - U.S.

We are seeking a Senior Technical Marketing Engineer (TME) to tell the technical story of Rafay’s GPU cloud platform, which GPU cloud providers, neoclouds, telcos, sovereign AI clouds, and enterprises use to turn GPU infrastructure into self-service, multi-tenant AI services. As a TME, you will bridge product, engineering, and go-to-market: building hands-on demos and reference architectures, publishing benchmarks, enabling field teams and partners, and feeding market insight back into the product.

You’ll work closely with product managers, engineers, tech pubs, solutions architects, ecosystem partners such as NVIDIA, and customers. The role spans the full stack: GPU and Kubernetes infrastructure, multi-tenancy and isolation, GPU partitioning and scheduling, AI/ML workbenches, model serving and inference monetization, and packaging it all into sellable service offerings. It is ideal for a hands-on engineer who enjoys running real workloads on real GPUs and distilling what they learn into content that drives adoption and customer success.

The work is ambitious and stimulating. You will be at the center of the AI infrastructure buildout, working on cutting-edge technology across accelerated computing, GenAI inference, and cloud native.

‍Key Responsibilities

Content & Messaging

• Develop technical collateral such as whitepapers, solution briefs, reference architectures, how-to guides, and demo scripts for audiences ranging from platform engineers to CTOs at GPU clouds.
• Create compelling technical presentations for webinars, sales enablement, partner events, and conferences (e.g., NVIDIA GTC, KubeCon).
• Turn each release into a clear narrative: what shipped, why it matters to GPU cloud operators and their tenants, and how to use it.

Demos, Labs & Reference Architectures

• Build and maintain GPU-backed demo environments and reproducible reference architectures (e.g., multi-tenant GPU clouds, AI workbenches, inference endpoints, bare metal and VM-based GPU offerings).
• Create hands-on labs and sandbox experiences for prospects, partners, and internal teams.
• Use simulation and emulation tooling (e.g., simulated GPU operators, virtual clusters) to demonstrate platform behavior at scale without dedicated hardware.

Benchmarking & Validation

• Design and run benchmarks for training and inference workloads (e.g., throughput, latency, time-to-first-token, GPU utilization) and publish defensible, reproducible results.
• Support ecosystem validation and compliance work with partners such as NVIDIA (e.g., NVIDIA AI Cloud-Ready, NVIDIA reference architectures).

Product & GTM Enablement

• Collaborate with Product and PMM on messaging and positioning across our GTM segments: NeoCloud, Enterprise, Telco, and Sovereign AI.
• Drive technical enablement for sales, solutions engineering, partner, and customer success teams (e.g., training modules, walkthroughs, battlecards, objection handling).
• Partner with alliances on joint solutions with GPU, OEM, and cloud-native ecosystem partners, and engage developer and operator communities in technical forums.

Market & Customer Intelligence

• Conduct hands-on competitive analysis of GPU cloud platforms, AI infrastructure software, and DIY approaches, and maintain comparison and differentiator assets.
• Gather feedback from POCs and field engagements with GPU cloud and enterprise customers to influence product direction.
• Identify recurring use cases and patterns (e.g., GPU-as-a-Service, AI workbenches, inference-as-a-service, sovereign AI clouds) and translate them into GTM assets.

Multimedia & Evangelism

• Record product demos and walkthrough videos to support campaigns, launches, and the website.
• Speak at virtual and in-person events, meetups, and industry conferences, and represent Rafay at partner and customer meetings.
• Write hands-on blog posts on GPU infrastructure topics (e.g., GPU partitioning with MIG, Dynamic Resource Allocation, disaggregated inference serving, NUMA-aware GPU VMs).

Technical Skills

• 6+ years of experience in technical marketing, solutions engineering, developer advocacy, platform engineering/SRE, or a related technical field.
• Strong hands-on Kubernetes experience and working knowledge of the NVIDIA GPU software stack (GPU Operator, drivers, CUDA, MIG, device plugins and DRA).
• Understanding of AI/ML workflows end to end (training, fine-tuning, and inference) and serving frameworks such as vLLM, SGLang, NVIDIA NIM, Triton, or Dynamo.
• Familiarity with GPU cloud building blocks: multi-tenancy and isolation, scheduling and quotas, high-performance networking (InfiniBand, RoCE), storage, and bare metal/VM provisioning.
• Ability to write and explain code and architecture (Python, YAML, Helm, Terraform, Bash, etc.).

Communication & Enablement

• Exceptional written and verbal communication skills for both technical and executive audiences.
• Experience creating and delivering technical presentations, live demos, and hands-on workshops.
• Ability to translate complex infrastructure capabilities into clear, outcome-oriented narratives (e.g., GPU utilization, time-to-revenue, tenant experience).

Collaboration & Execution

• Comfortable working cross-functionally across product, engineering, marketing, sales, and partners.
• Proven ability to manage multiple deliverables and deadlines in a fast-paced environment.
• Self-starter who thrives on autonomy, impact, and creative freedom.

Desired Skills

• Experience at a GPU cloud, neocloud, hyperscaler, or AI infrastructure vendor.
• Hands-on experience with LLM benchmarking tools (e.g., NVIDIA AIPerf/GenAI-Perf, MLPerf) and GPU observability (DCGM, Prometheus, Grafana).
• Exposure to accelerators beyond NVIDIA (e.g., AMD Instinct).
• Contributions to open-source projects or communities in the Kubernetes and AI ecosystem (e.g., CNCF, Kubeflow, KServe, Ray).
• Familiarity with producing and editing video or webinar content.
• NVIDIA certifications are a plus.

Travel

• Up to 20% travel for industry conferences, partner engagements, and customer meetings.

WHY JOIN RAFAY

Rafay is at the forefront of GPU cloud and Kubernetes technologies. GPU cloud providers, telcos, sovereign AI clouds, and enterprises use Rafay to deliver AI infrastructure as a service, and as our TME you will shape how the market understands that platform. We work in a collaborative environment that rewards creative thinking and provides opportunities to advance professional careers in advanced technology development. On top of this, we offer a solid work/life balance, a fun and dynamic work environment, a competitive salary, robust benefits and attractive stock options. As the first of our kind, we are truly in a class of our own.