Staff SDET, AI Gateway

Join the Future of Security at Netskope Netskope (NASDAQ: NTSK) is a leader in modern security and networking for the cloud and AI era. We secure and accelerate cloud, data, and AI in real time, everywhere. Thousands of customers, including more than 30 of the Fortune 100, trust the Netskope One platform, its Zero Trust Engine, and the powerful NewEdge network to gain full visibility and control without performance trade-offs. At Netskope, our technology is driven by our greatest strength: our people. We believe that belonging powers innovation, and success is both personal and organizational. We embrace differences in gender, ethnicity, beliefs, ability, and identity, creating an environment where every voice is heard and respected. We empower our employees to bring their authentic selves to work, grow their careers through continuous education and mentorship, and lead with transparency and curiosity. Join a team where you belong, where you are encouraged to be an entrepreneur, and where together, we continue to redefine the landscape of security. Visit Careers at Netskope to learn more. Follow us on  LinkedIn and  Instagram . Please note, this team is hiring across all levels and candidates are individually assessed and appropriately leveled based upon their skills and experience. About the Role Netskope AI Gateway helps global enterprises securely adopt and scale Generative AI. We are looking for a Staff Software Development Engineer in Test (SDET) to own quality for significant areas of the AI Gateway product, its supporting services, and its APIs, across SaaS and on-prem/hybrid customer environments. This is a hands-on individual-contributor role with real ownership. The areas assigned to you are yours: you set the testing direction and build what is needed to execute it, bringing hard calls to the QE lead as a peer rather than waiting for a specification or a checklist before you act. Depth matters more than volume in this role. We are looking for someone who can get a long way into an ambiguous problem before needing help, go deep when a failure crosses service, infrastructure, and network boundaries, and work directly with developers and product managers to get the underlying problem fixed rather than routed around. What You Will Be Doing Own Quality for Your Areas: For the features and services assigned to you, own the outcome: reliability, correctness, performance, security-related behaviors, and user experience expectations. Decide what needs testing and at which layer, set the bar for shipping that area, and be accountable for what gets through. Turn Ambiguity into a Plan: Start from partial requirements, an architecture diagram, and a conversation with the developer, and produce a concrete test approach. Surface the risks nobody has written down yet, decide which ones matter, and state clearly what you are choosing not to cover and why. Build the Automation: Design and implement the automated suites and the frameworks, harnesses, and fixtures behind them. Write them as real software: readable, maintainable, and quick to triage when they fail. Keep flakiness low through deterministic test design, environment isolation, and disciplined use of mocking and service virtualization. Use AI Where It Earns Its Place: Bring AI and agent-based tooling into the testing workflow: generating scaffolding and test data, navigating unfamiliar code, summarizing logs and failures, and shortening the path from symptom to hypothesis. Be equally clear about the limits. AI-generated tests can look thorough while asserting nothing that matters, so you check what the assertions actually prove. The tool can draft the work, but the coverage decision and the verdict stay yours. Validate the Gateway Data Path: Verify that AI Gateway stays compatible with the provider APIs it fronts, that policy matches exactly the requests it should, and that the resulting action and logging are correct. Debug Deep and Across Boundaries: Be the person who gets to the bottom of hard failures. Trace problems across microservices, asynchronous workflows, Kubernetes networking, TLS and proxy behavior, and CI infrastructure, and come back with a root cause rather than a reassignment. Keep CI/CD Trustworthy: Integrate your tests into CI/CD with attention to signal-to-noise, parallelization, execution time, and reporting that points at the actual failure. Diagnose pipeline and environment problems instead of retrying around them. Share Production Ownership: Own the production service alongside the rest of the AIG engineering team. Take part in the shared on-call rotation, monitor production health, and troubleshoot and resolve critical production issues. Push Problems Upstream: Work directly with developers and product managers. Raise testability and instrumentation needs while designs are still open, argue for the fixes that prevent whole classes of defects, and make sure recurring failure patterns change how the next fe