Senior Java Software Engineer in Test

This is a Full Time Job

Senior Software Engineer (Java) in Test

#WeAreParamount on a mission to unleash the power of content… you in?
We’ve got the brands, we’ve got the stars, we’ve got the power to achieve our mission to entertain the planet – now all we’re missing is… YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture.

Job Title: Senior Software Engineer in Test

Team: Global Quality Engineering

Location: New York City, Los Angeles, San Francisco

Overview

Paramount Skydance Corp. is seeking a Senior Software Engineer in Test to design, architect, and deliver high-quality, scalable, and reliable automated testing solutions across our enterprise platforms. This role requires proficient skills in Java engineering. It also requires experience in advanced test automation and knowledge of the media and streaming industry. You will use modern CI/CD practices to improve quality and reduce risk. Your work will help provide great customer experiences on CTV, SmartTV, mobile, and web platforms.

As a member of the Global Quality Engineering (GQE) organization, you will utilize our existing departmental automation framework, help drive quality strategies, and mentor engineers across GQE to elevate testing maturity and engineering excellence. You will also leverage AI-augmented development and testing practices using Claude Code, GitHub Copilot (in-IDE), Cursor, and similar tools.

Key Responsibilities

Create Robust & Reliable Automation Coverage across front-end and back-end

• Create and maintain automated tests using 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.

• Utilize 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. • Ensure consistent adoption of engineering best practices, coding standards, and high-quality test development patterns.

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
• 6 years of experience in software engineering or quality engineering with solid 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
• 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

• Quality mindset – commitment to reliability, maintainability, and continuous improvement.

• AI fluency – practical judgment on where AI adds measurable value in testing.

• Systems thinking – understanding distributed systems and complex media pipelines.

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.

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