Staff Software Engineer (Enterprise)

Relevance AI
Sydney, AustraliaPosted 9 March 2026

Job Description

Staff Software Engineer (Enterprise) Location 📍: Sydney, AU, Hybrid (3 days a week in office) About Us 🚀 At Relevance AI, we’re building the home of the AI workforce. Our mission is simple: empower every team to delegate meaningful work to AI agents that think, act, and collaborate like experts. With Relevance AI, anyone can create and manage intelligent agents that handle workflows, decisions, and collaboration - all within one unified platform. Our technology already powers industry leaders such as Canva, Databricks, Confluent, Autodesk, Lightspeed, Rakuten, Aveva, Qualified, and Activision Blizzard, helping them scale excellence across operations, marketing, and sales. We’re backed by Bessemer Venture Partners, Insight Partners, Peak XV, and King River Capital, and raised our Series B in April 2025 to accelerate growth and push the boundaries of agentic automation. Headquartered in San Francisco and Sydney, we operate on a hybrid model and thrive on curiosity, collaboration, and execution - we move fast, think big, and win together. In 2025, we were proud to be named LinkedIn’s #1 Startup in Australia. If you want to define how the world works with AI, join us. The Role 🥳 We’re hiring for a Staff Software Engineer to join our mission-critical Enterprise team in Sydney. This team builds and scales the core platform capabilities that power Relevance AI’s largest and most demanding customers. You’ll work on the systems that make our platform secure, reliable, compliant, and extensible at enterprise scale - from identity and access management to auditability and compliance. You will drive deep technical leadership and execution across the Enterprise team, partnering closely with product, engineering, and go-to-market teams to deliver robust enterprise-grade capabilities and back-end systems. This is a high-visibility role where you’ll have significant autonomy to design complex systems, shape architecture across teams, and see your impact reflected in real customer outcomes. Each engineering team at Relevance AI is tackling a different frontier of the AI workforce, from enabling individual builders to scaling enterprise-grade AI systems used by thousands of employees globally. This role sits at the heart of that mission, building the systems that make AI usable, reliable, and production-ready. At Relevance AI, your work will reach customers within hours, offering a rare opportunity to see the impact of your contributions in real-time. You’ll have autonomy and trust to lead key projects and mentor team members without being weighed down by unnecessary layers of approval. You’ll also work across our modern modern tech stack, which we’re constantly optimising to move and iterate faster. Our supportive, fast-paced culture fosters collaboration, curiosity, and creativity, making it an environment where innovation thrives and where your ideas and expertise will be highly valued. Your Impact 💥 - Lead design and implementation of enterprise-grade features and back-end systems relating to SSO, RBAC/permissions, audit logs, compliance tooling, and data visibility. - Drive key initiatives around reliability, scalability, performance, and security to support enterprise adoption. - Translate customer and business requirements into robust technical designs in collaboration with product and go-to-market teams. - Own projects end-to-end - from system design through to deployment and long-term maintenance. - Contribute to platform architecture and technical strategy as the enterprise surface area of the product expands. - Set and uphold high standards for code quality, maintainability, observability, and security. - Mentor engineers, share best practices, and help raise the overall bar for back-end excellence. Our Tech Stack (so far) 💻 We leverage a modern, scalable, and developer-friendly technology stack to build robust web applications: - Frontend: Vue.js, Nuxt, Typescript - Backend: Express.js, Node.js, Type ... (truncated, view full listing at source)
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