OpenRouter
Customer Engineer
About this role
OpenRouter seeks a Customer Engineer to serve as the technical lead during enterprise sales cycles, partnering with account executives to conduct discovery, deliver tailored demos, and address technical concerns. You'll bridge sales, engineering, and product teams while advising prospects on AI solutions and gathering market feedback to shape the product roadmap.
What you'll do
- Lead technical discovery calls to understand prospects' AI/ML architecture, use cases, and integration requirements
- Design and deliver customized product demonstrations and proof-of-concept environments tailored to specific customer needs
- Assess technical fit including API integration, model compatibility, data residency, and compliance requirements
- Partner with account executives throughout the sales cycle, presenting at customer meetings and executive briefings
- Address technical objections, competitive positioning concerns, and negotiate technical terms and SLAs
- Provide product feedback to engineering teams on feature requests, competitive gaps, and market requirements
What they're looking for
- Sales engineering or solutions architecture in B2B SaaS/API platforms
- Software development and hands-on API or cloud infrastructure experience
- Technical presentation skills for diverse audiences
- Solution architecture and problem-solving under pressure
- AI/ML technologies and LLM platforms (preferred)
- Customer success mindset and consultative selling
- Product-market fit analysis
- Contract negotiation on technical terms
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OpenRouter
OpenRouter builds a platform that routes and manages high-volume requests across language models and AI inference services. The company is hiring software engineers for platform infrastructure and product development, as well as forward-deployed engineers to support customer implementation and scaling.
- Website
- openrouter.io
Likely interview questions
- Walk us through your experience as a sales engineer—what was your most complex technical deal and how did you architect the solution?
- How do you approach explaining complex AI/ML concepts to non-technical executives versus technical decision-makers?