Senior Technical Marketing Engineer

We are looking for a hands-on technical marketing professional who can translate deep infrastructure knowledge into commercial outcomes. In this role you will report to the SVP of AI Product & Business, spanning hands-on technical enablement, partnership development, and go-to-market execution of our flagship KV cache acceleration product, Inferra. You will partner closely with the engineering team (architecture, deployment, solutions) to turn technical capability into content, guides, and technical partnerships that drive adoption. You will own the technical story of Inferra from product positioning and market education through POVs, proof points, sales/channel enablement and field execution. You will work directly with prospective customers to help them successfully evaluate and implement Inferra, capture the technical and business outcomes of those deployments, and turn those learnings into compelling customer-facing content and sales assets This position is located in Central/ Midwest, USA. Responsibilities Deployment & technical enablement: Build and maintain deployment guides, reference architectures, and technical documentation for NeoCloud and Inference customers evaluating or deploying our products Hands-on validation: Deploy and debug Kubernetes clusters and inference stacks yourself to validate claims, reproduce customer issues, and keep collateral technically authentic. Partnerships: Identify, develop, and manage strong technical relationships with NeoClouds, GPU cloud providers, and ecosystem players (inference frameworks, hardware vendors) to expand product footprint Technical marketing content: Translate performance data (KV cache hit rates, TTFT, session density, cost-per-token) into customer-facing narratives, briefing decks, and competitive positioning Sales enablement: Create battle cards, ROI/pricing tools, and technical FAQs that help the sales team and partners sell the product credibility Community engagement: Contribute to technical community engagement (GitHub, Hugging Face, vLLM/SGLang communities) GTM execution: Conference demos, technical collaterals, blogs and website content. Qualifications Prior Experience in the technology domain: 5+ years Bachelors in Computer Science or Electronics/Electrical engineering, MBA is a plus Experience in the AI infrastructure space, preferably at a NeoCloud, GPU cloud provider, or inference software company Hands-on experience deploying and operating Kubernetes clusters, including debugging real production issues Working knowledge of the inference stack—including KV cache, GPU memory constraints, and throughput/latency tradeoffs— enough to have a detailed technical conversation with an ML infra engineer Track record of applying infrastructure efficiency improvements through software stack optimization. Familiarity with inference serving frameworks like vLLM, SGLang, or others Strong business acumen: comfortable with partnership negotiation and commercializing technical products Excellent verbal and written communication — able to produce clear, technically accurate customer-facing content Startup mentality: High ownership, comfortable with ambiguity, and willing to do work above and below your core domains Prior technical marketing, application or solutions engineering experience in AI/ML infrastructure or AI model efficiency teams will be a plus Existing relationships in the NeoCloud/Inference-as-a-Service ecosystem and GPU cost/performance modeling is a plus