Customer Engineer

What You'll Do

• Serve as the named technical point of contact for a portfolio of Render's highest-value customers—primarily AI-native startups, technology companies, and scaled SaaS businesses
• Lead structured onboarding for new customers: architecture reviews, environment setup, team enablement, and establishing production readiness standards from day one
• Proactively monitor customer health signals—service stability, resource utilization, deployment frequency, support ticket patterns—and intervene early to prevent performance degradation and churn
• Conduct regular technical check-ins and executive business reviews (EBRs/QBRs) with engineering and infrastructure leaders, translating platform usage data into business-relevant insights
• Develop deep familiarity with each customer's architecture and growth trajectory to identify natural expansion opportunities: additional services, higher compute tiers, Postgres scaling, team seat growth, and new workload types
• Collaborate with Account Executives to surface expansion signals, build joint account plans, and execute upsell and cross-sell motions grounded in genuine technical advice
• Drive adoption of new Render capabilities—especially those relevant to AI/ML workloads such as autoscaling for inference, high-throughput background job pipelines, and preview environments
• Help customers design and optimize infrastructure for AI-native applications: LLM API integrations, vector database connections, async inference queues, streaming endpoints, and multi-service orchestration
• Capture and escalate structured product feedback from customers, creating a feedback loop that shapes the roadmap based on what matters most to Render's top accounts
• Work with Support and Engineering on complex escalations, acting as the customer's technical advocate to drive timely resolution while maintaining trust and transparency

What We're Looking For

• 8+ years in a post-sales technical role—Customer Engineer, Technical Account Manager, Solutions Architect (post-sales), or Customer Success Engineer—at a cloud infrastructure, developer tools, or SaaS company
• Strong hands-on understanding of cloud application architecture: web services, background workers, databases, caching, networking, and container deployments—enough to review a customer's setup and offer substantive guidance
• Demonstrated experience working with AI-native or AI-adjacent companies, with familiarity in LLM API integration patterns, vector search, and the infrastructure requirements of production AI applications
• Proven commercial acumen: identifying expansion opportunities, contributing to renewal and upsell motions, and tracking account health metrics including NRR and time-to-value
• Exceptional communicator—can explain a complex infrastructure tradeoff to a CTO and send a clear Slack message to a developer debugging a deployment with equal ease
• Can manage a portfolio of technically complex accounts simultaneously, maintaining proactive cadences without letting reactive firefighting dominate their time
• Collaborative by instinct: works well with Sales, Product, Engineering, and Support, and understands that customer success is a team sport

Nice-to-Haves

• Direct experience as a software engineer or DevOps engineer, especially having run production workloads on a PaaS like Render, Heroku, or Railway
• Experience supporting customers through a Heroku-to-Render (or similar PaaS) migration, including large database cutovers and multi-service re-architecture
• Familiarity with the modern AI application stack: LangChain, LlamaIndex, OpenAI or Anthropic APIs, Pinecone, Weaviate, Celery, or equivalent tools
• Background working with high-growth, VC-backed startups as customers—understanding their pace, funding cycles, and how infrastructure decisions intersect with business milestones
• Experience building customer-facing technical content: architecture guides, best practice playbooks, or internal runbooks that scale impact beyond 1:1 conversations
• Exposure to usage-based or consumption-based pricing models and how to help customers optimize cost as they scale