Netskope: Staff SDET, AI Gateway — Taipei, Taiwan

About Netskope

Netskope develops security and networking technology for cloud, data and AI environments. Its employer introduction identifies the Netskope One platform, a zero-trust engine and the NewEdge network as parts of that offering. The business describes giving organisations visibility and control while maintaining the performance of access to applications and information. This connects cybersecurity with networking: the products address both how digital resources are reached and how their use is controlled and protected.

The position and its setting

Netskope lists Staff SDET, AI Gateway in Taipei, Taiwan, under reference 8168624. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Department: Cloud Data Path
• Employer listing date: 1 September 2026

Responsibilities and work described

• Netskope AI Gateway helps global enterprises securely adopt and scale Generative AI
• 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
• Depth matters more than volume in this role
• 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
• Building: 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

Essential requirements and candidate background

• 8+ years in software quality engineering / SDET / test automation, including 5+ years on distributed systems, cloud services, or security/networking products
• You have owned quality for a substantial product area with little supervision
• Autonomy: Track record of taking an under-specified problem and driving it to a working solution, including deciding what not to do
• Comfortable being the only person working on something
• You write automation, libraries, and tooling that hold up as production software rather than as scripts
• AI-Assisted Engineering: Practical experience using LLM-based tools or coding agents in real engineering work, and a clear view of where they help and where they mislead
• You can point to a case where you caught what the AI missed
• Debugging Depth: Demonstrated ability to isolate root cause in complex failures spanning services, infrastructure, and the network layer, rather than escalating them onward
• API and Distributed Systems Testing: Extensive hands-on experience testing REST APIs, microservices, and asynchronous workflows
• Test Design: Deep command of test strategy, risk-based testing, and layered test design; able to make and defend explicit trade-offs on coverage, cost, execution time, and confidence
• CI/CD: ability to build and troubleshoot CI/CD pipelines and the infrastructure that keeps large test suites reliable
• Containers and Kubernetes: experience running and testing services on Docker/Kubernetes, and debugging failures involving networking, service discovery, and configuration
• Networking: working knowledge of HTTP/S, TLS, proxy/gateway concepts, DNS, and the failure modes that affect AI Gateway deployments
• Defect Management: defect isolation, triage, and tracking discipline (e.g., Jira), with follow-through until the systemic fix lands
• Working with Engineering and Product: Able to hold a technical discussion with developers as a peer and to explain risk and trade-offs to product managers clearly
• Verbal and written English for complex technical collaboration

Preferred experience and additional strengths

• Programming: software engineering skills in Python (preferred) or Golang
• GenAI Ecosystem: Familiarity with the request and response schemas of major LLM provider APIs, such as OpenAI, Anthropic, Google, or Bedrock, including their streaming formats and how these APIs change over time
• Cloud Platforms: Experience with AWS, Azure, or GCP, and validating hybrid/on-prem customer deployments
• Cybersecurity: Knowledge of secure web development, ZTNA, DLP, and proxy/gateway product concepts
• Performance and Scale: Experience designing load and performance tests and analyzing latency/throughput for high-traffic APIs and gateways
• Resilience Testing: Fault injection, chaos, or failure-mode testing of distributed services
• Observability: Hands-on use of metrics/logging/tracing tooling (e.g., Prometheus, Grafana, OpenTelemetry) for faster test and defect triage
• Education
• BS in Computer Science or equivalent degree required
• MS in Computer Science or equivalent is strongly preferred
• #LI-SK3
• Netskope is committed to implementing equal employment opportunities for all employees and applicants for employment

Location, schedule and contract conditions

For this overseas or regional listing, confirm work authorisation and any location restrictions with the employer. The location does not establish visa sponsorship or permission to work remotely from India.

• Advertised location: Taipei, Taiwan

Conditions requiring the complete notice

Some passages contain complex or combined conditions that have not been reduced to short eligibility statements here. Read the complete linked notice for those passages, including any alternatives, exceptions or restrictions. The concise facts above should not be treated as the full wording of every condition.

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another Staff SDET, AI Gateway vacancy is not an offer for reference 8168624; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Netskope

Applications follow the official employer page linked below.

Open the official Netskope posting below and match the title and reference 8168624.

Compare your qualifications and experience with the essential conditions for the Taipei, Taiwan position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• Kubernetes
• CI/CD
• Docker
• AWS
• GCP
• Azure
• REST APIs
• APIs
• AI/ML
• LLMs
• ISO
• Google Cloud
• AI