Lead Software Engineer in Test (SDET)
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- Closed · 30d
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- All qa / test
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Nex is on a mission to help families rediscover the joy of movement. Created by parents for parents, Nex combines technology and play to deliver fun, social, and interactive experiences powered by natural body motion, encouraging kids and adults to move more, play more, and have fun together. Nex Playground, the company’s award-winning active play system, is purpose-built to get families moving year-round, with safety and privacy as core considerations in its intentional design. It is certified kidSAFE+ COPPA compliant and built to support healthy, active play for all ages and abilities.
Nex Playground features a growing library of 50+ experiences, including motion and dance games, fitness and educational experiences, and Nex Originals. Content includes collaborations with partners like Hasbro, Sesame Workshop, and NBCUniversal. Nex has been recognized by Fast Company’s Most Innovative Companies, TIME’s Best Inventions, and Parents’ Best Entertainment System for Families, and has earned Red Dot, IDEA, and Core77 international design awards. We encourage you to explore Have Fun and Is Motion Gaming Back?, as they offer a deeper look into our culture, values, and explain how our approach to motion gaming differs from previous generations.
Nex employees may be expected to collaborate with colleagues across different time zones. Depending on the role and location, occasional travel may also be required for company events, external meetings, and other business-related activities.
Important Notice: ⚠️ To protect yourself from recruitment scams, please note that Nex communicates through official company emails ending in @nex.inc. If you receive a suspicious email or message, please do not respond and report it immediately to us.
Location: Hong Kong
The Role
As a Lead Software Engineer in Test (SDET) at Nex, you will build the test engineering capability within Platform Software Engineering that helps us ship reliable Nex Playground experiences.
You will define and implement an automated test strategy spanning device software, cloud services, and hardware-integrated systems. From functional and regression testing to sustained device stress and cloud-scale load, you will design and build the tools, practices, and feedback loops that help engineers find problems early and release with confidence. You will lead through technical judgment, hands-on execution, and collaboration, and drive practical AI adoption in your work.
Success Looks Like
- Teams can rely on fast, maintainable automated tests to detect meaningful regressions throughout development and release.
- Device software, hardware-integrated systems, and cloud services are tested under realistic conditions, including sustained stress and load at scale.
- Engineers can see test coverage, reliability trends, and release risks clearly, and use that information to improve products and test effectiveness.
- Test tools and methods continuously improve, and platform engineers can design and build more testable systems.
What You'll Do
Test strategy and technical leadership
- Own and evolve the automated test strategy for Platform Software Engineering. Prioritize coverage based on product risk and drive a high level of meaningful automated test coverage.
- Define test approaches, quality criteria, and release feedback with platform engineers working on device software and cloud services.
- Review and improve vendors’ test strategies and coverage, identify gaps, and work with vendors to address them.
- Research, evaluate, and develop new testing techniques and tools that improve coverage, speed, and confidence.
- Drive responsible, practical AI adoption in test design, automation, analysis, and engineering workflows, while maintaining human judgment and verification.
Automation and test infrastructure
- Develop and operationalize functional, stress, and regression testing for software and hardware products, using both software-based and hardware-integrated techniques.
- Develop and operationalize cloud service load testing using synthetic workloads and data at scale; identify bottlenecks, failure modes, and regressions.