Responsibilities
- Define and own Tubi's multi-platform automation strategy — covering Web, iOS, Android, CTV (Roku, Fire TV, Apple TV, Smart TVs, game consoles), and API layers.
- Establish testing standards, coverage targets, and quality gate policies across the CI/CD pipeline to protect release confidence and production reliability.
- Design specialized test strategies for business-critical scenarios: video playback (HLS/DASH), ad insertion, content recommendation surfaces, and user authentication flows.
- Use AI-assisted analysis (e.g., failure pattern clustering, test gap detection) to continuously improve test strategy based on real production signal and defect trends — not gut instinct.
- Lead the design and maintenance of scalable, cross-platform automation frameworks built for Tubi's unique device matrix and streaming use cases.
- Ensure automation suites are reliable, fast, and integrated as quality gates in CI/CD — owning execution health, not just test coverage numbers.
- Continuously build and evolve AI-augmented engineering workflows/system that fundamentally change how the team creates, maintains, and improves automation — going beyond adopting existing tools to designing new patterns, custom integrations, and repeatable practices that raise the team's output ceiling.
- Own the team's AI capability growth: identify high-leverage AI applications in the automation lifecycle (e.g., AI-assisted test case generation, intelligent flaky cases detection, regression root cause summarization), prototype them, and codify them into team standards.
- Evaluate and selectively adopt emerging testing tools and AI-for-testing innovations. Translate promising external ideas into concrete, team-specific workflows, and make sure adoption leads to measurable improvements in speed, reliability, and engineering quality.
- Manage and grow a team of automation engineers: set clear individual goals, run regular 1:1s, create concrete career development plans, and hold a high bar for technical execution.
- Actively mentor engineers on AI-augmented engineering practices — including prompt engineering, AI-assisted debugging, and responsible review of AI-generated code — so that AI becomes a multiplier, not a shortcut.
- Drive consistent AI tool adoption across the team; share workflows and learnings org-wide to spread impact beyond the immediate team.
- Foster a culture of continuous improvement: encourage experimentation, normalize blameless retrospectives, and track team health alongside delivery metrics.
- Partner with QA leads, engineering managers, and product managers to clarify testing requirements, flag quality risks early, and align on release readiness criteria.
- Represent automation engineering in cross-functional planning, contributing to roadmap discussions with data on quality gaps, automation ROI, and risk areas.
- Produce regular, data-driven quality status reports for leadership, covering defect trends, automation coverage, execution reliability, team velocity, and AI-driven insights such as flaky test clusters, recurring failure patterns, and release risk signals.
- Participate in incident reviews for production quality regressions, driving root cause analysis and ensuring systematic prevention — not just one-off fixes.
Requirements
- Bachelor's degree or above in Computer Science, Software Engineering, or a related technical field.
- 10+ years of experience in software testing and quality engineering, with at least 3 years in a technical leadership or team management role.
- Experience building AI agents, skills, workflows, or similar Native AI based automation systems. Can combine multiple AI tools for complex tasks and proactively identifies where AI augmentation reduces toil or improves accuracy.
- Hands-on expertise with cross-platform automation frameworks (e.g., Puppeteer, Playwright, Appium, WebDriverIO, Selenium, PyTest, TestNG, Jest) — and the judgment to know when to adopt a new tool vs. improve what exists.
- Proficient in at least one primary programming language — JavaScript/TypeScript (preferred), Python, or Java — and comfortable reading code across stacks.
- Solid grasp of client and server application architecture, enabling effective test design at the system boundary — not just at the UI surface.
- Strong understanding of CI/CD systems (e.g., GitHub Actions, Jenkins, Buildkite) and experience integrating automation into complex, multi-stage pipelines.
- Strong product sense: able to identify quality risks from a user and business perspective, not just a coverage-metrics perspective.
- Excellent communication skills — writes clearly, presents data persuasively, and drives alignment in cross-functional settings.
- Led the end-to-end design and rollout of a large-scale automation framework (new build or major migration).
- Multi-platform testing experience across Web, Mobile, and Smart TV / CTV / OTT device ecosystems.
Nice to Have
- Hands-on experience applying AI to automation tasks: test case generation, log summarization, flaky test analysis, quality dashboard construction, or regression triage automation.
- Experience coaching engineers or QA partners on responsible, effective AI usage in daily engineering work.
- Familiarity with video playback, streaming protocols (HLS/DASH), or ad insertion testing scenarios.
- Prior experience in the streaming, media, or consumer tech industry.
- Experience with distributed system testing or large-scale performance and load testing.
