About the role
About OpenRouter OpenRouter is the unified interface for AI inference, giving developers and organizations access to hundreds of models through a single API. Our platform helps teams choose, route, evaluate, and scale model usage across providers, making it easier to build production AI applications with the right model for every use case. As inference becomes a core operating layer for modern software companies, OpenRouter helps customers move faster, reduce complexity, and get more value from the rapidly evolving model ecosystem.
About
the Role We are looking for a Forward Deployed Engineer to join our post-sales team and help customers successfully implement, adopt, and scale OpenRouter. This role sits at the intersection of customer success, support, customer engineering, product, and developer relations. You will work directly with our customers after the sale to help them get live quickly, integrate OpenRouter into production systems, improve their inference architecture, and expand usage across teams and applications. You will be deeply hands-on with developers. You will debug integrations, design implementation paths, advise on model and routing strategy, identify opportunities to increase adoption, and turn customer learnings into product feedback. The ideal candidate is technical enough to earn the trust of engineering teams, commercial enough to understand customer outcomes, and proactive enough to drive customers from initial implementation to scaled production usage. What You’ll Do Customer Implementation Lead technical onboarding for new customers after contract close, helping them move from kickoff to production usage. Partner with Customer Success Managers and Account Executives to define success plans, implementation milestones, and technical adoption goals. Help customers integrate OpenRouter into their applications, workflows, infrastructure, and developer environments. Troubleshoot API, SDK, model selection, routing, latency, reliability, and deployment issues. Create implementation guides, reference architectures, code examples, and reusable playbooks for common customer patterns. Inference Adoption and Optimization Work with customers to understand their AI use cases, application architecture, traffic patterns, and inference goals. Recommend model, provider, routing, fallback, caching, and cost-optimization strategies based on customer needs. Analyze usage patterns to identify opportunities to increase adoption, improve reliability, reduce cost, or expand into new workloads. Help customers evaluate models and providers across quality, latency, cost, reliability, privacy, and scale