Lead Software Engineer (Java) in Test

Lead Software Engineer (Java) in Test

Paramount Skydance Corp. is seeking a Staff Software Engineer in Test to design, architect, and deliver high-quality, scalable, and reliable automated testing solutions across our enterprise platforms. This senior technical role combines deep Java engineering expertise, advanced test automation, media/streaming domain knowledge, and modern CI/CD practices to accelerate quality, reduce risk, and ensure world-class customer experiences across CTV, SmartTV, mobile, and web.

You will be a senior technical leader within the Global Quality Engineering (GQE) organization. You will use and enhance our existing departmental automation framework, define quality strategies, and mentor engineers across GQE to elevate testing maturity and engineering excellence. You will also drive AI-augmented development and testing practices using GitHub Copilot (in-IDE), Cursor, and similar tools.

Key Responsibilities:

• Automation Framework Architecture
• Enhance and extend our existing Java-based departmental test automation framework supporting API, UI, Mobile, CTV/SmartTV, and integration testing; introduce reusable libraries, utilities, and patterns to improve stability and scale.
• Establish scalable patterns for reusable test components, data management, assertions, parallelization, and distributed execution.
• Integrate the framework deeply into CI/CD pipelines to support shift-left testing and continuous quality.
• AI-Augmented Quality Engineering
• Champion AI-assisted development and testing using tools such as GitHub Copilot (IDE), Cursor, and LLM-based assistants to improve developer productivity, test authoring velocity, code reviews, and defect triage.
• Apply prompt engineering and retrieval techniques to accelerate test case generation, log/telemetry summarization, video-quality anomaly detection, and root-cause analysis.
• Evaluate and integrate functional AI capabilities that measurably reduce flakiness, false positives, and triage time within the existing framework.
• Media & Multi-Platform Quality
• Expand automation coverage for video playback, streaming resiliency, DRM flows, SSAI/CSAI ad insertion, captions/subtitles, and content protection.
• Validate QoE metrics (startup time, rebuffering ratio, frame drops, video/audio sync, error recovery) across Roku, Fire TV, Android TV/Google TV, Samsung Tizen, LG webOS, Apple TV, iOS/Android mobile apps, and modern web players.
• Leverage media tooling such as ffmpeg/ffprobe, Charles/Fiddler, and player inspection APIs for diagnostics and validation.
• Quality Engineering Technical Leadership
• Define technical strategy for automated testing across services, microservices, UIs, mobile apps, and OTT/CTV applications leveraging the departmental framework.
• Ensure consistent adoption of engineering best practices, coding standards, and high-quality test development patterns.
• Lead the evaluation and adoption of modern automation technologies and quality platforms.
• Cross Functional Collaboration
• Partner with Video Engineering, Playback/Player, Ads/Monetization, CDN/Streaming Operations, and Platform teams to ensure robust validation of streaming experiences and ad workflows.
• Collaborate with Product to ensure test coverage aligns to business priorities and quality goals.
• Work closely with Platform and Data Engineering to support scalable test environments, quality data, and telemetry.
• Technical Execution
• Design and implement complex automated test suites using Java, Selenium, Appium, JMeter, Locust, REST-Assured, TestNG/JUnit, and related tooling within the existing framework.
• Build and optimize testing strategies including API/contract, integration, performance/reliability, and device-level testing for CTV/SmartTV, mobile, and web.
• Diagnose and tackle issues related to automation reliability, environment instability, device fragmentation, and system integration.

Required Qualifications:

• 7+ years of experience in software engineering or quality engineering with robust Java development expertise.
• Demonstrated experience using and enhancing an existing enterprise/departmental automation framework.
• Deep expertise in common test automation tools (Selenium, Appium, Playwright, REST-Assured, TestNG, JUnit, Maven/Gradle).
• AI experience (must-have): hands-on use of AI-assisted development tools. This includes tools like Claude Code, GitHub Copilot (IDE), Cursor, or similar. Candidates should also have useful prompt engineering skills for code and tests. LLMs are leveraged for tasks such as generating tests, suggesting code reviews, summarizing logs, and speeding up triage.
• Strong knowledge of CI/CD tools (Jenkins, GitHub Actions, GitLab CI, Argo, Spinnaker) and quality gates.
• Proven experience testing media/streaming applications across CTV (Roku, Fire TV, Android TV/Google TV), SmartTV (Tizen/webOS), iOS/Android mobile, and web.
• Hands-on validation of HLS/DASH, ABR, DRM, captions/subtitles, and QoE metrics.
• Cloud experience (GCP, AWS, or Azure) and containerization (Docker, Kubernetes).
• Solid knowledge of software design patterns, data structures, and core engineering principles.

Preferred Qualifications:

• Experience mentoring engineers or leading cross-functional automation initiatives.
• Familiarity with observability and APM tools (Grafana, Prometheus, NewRelic, DataDog) and media telemetry(Conviva, MUX, NPAW etc).
• Experience with contract testing (PACT), service virtualization, or synthetic media test data.
• Exposure to CTV/SmartTV automation stacks and remote device labs.

Key Competencies:

• Technical leadership – ability to guide teams and evolve automation coverage.
• Quality mindset – commitment to reliability, maintainability, and continuous improvement.
• AI fluency – judgment on where AI adds measurable value in testing.
• Systems thinking – knowledge of distributed systems and complex media pipelines.
• Ownership – ability to drive initiatives end-to-end with autonomy.

Success Metrics:

• Increased automation coverage across CTV/SmartTV, mobile, and web; reduced manual regression.
• Measurable reduction in escaped defects and media playback incidents.
• Improved stability and reliability of automated pipelines and device runs.
• Faster release cycles through scalable, AI-augmented automation.
• Broad adoption and consistent usage of the GQE test framework across teams.