Android AI Systems SDET

Join a pioneering engineering team driving the evolution of intelligent systems across one of the world's most widely used technology ecosystems. As an AI Systems SDET, you'll be part of a high-impact program powering next-generation conversational and on-device intelligence that runs seamlessly across mobile, app, and cloud environments.

In this role, you'll work hands-on to ensure the performance, stability, and correctness of AI system integrations across Android and broader software stacks. You'll design automation frameworks, build quality pipelines, validate end-to-end AI features, and transform data into insights that enhance how billions of users experience intelligent computing.

Key ResponsibilitiesAI System Validation & Debugging

• Validate AI functionality across Android platforms, applications, and distributed systems for stability, compatibility, and correctness.
• Collaborate with developers to design and refine AI evaluation datasets, improving model performance, data coverage, and quality.
• Conduct system-level testing and deep debugging using tools like ADB, logcat, and tombstone to identify and resolve root causes of anomalies.
• Execute edge-case and exploratory testing to uncover reliability issues in AI-driven experiences.

Performance & Benchmarking

• Run and analyze performance and compatibility benchmarks (e.g., ACT suites, custom microbenchmarks).
• Monitor system performance across multiple layers (device, middleware, app) to detect regressions and resource inefficiencies.
• Evaluate key metrics such as latency, throughput, and memory usage, and provide optimization recommendations.

Automation, Data & Quality Insights

• Develop automation frameworks and pipelines to increase coverage, repeatability, and reliability of AI system testing.
• Write and optimize SQL queries and Python scripts to analyze test data and system logs, surfacing actionable quality metrics.
• Build internal dashboards to visualize test results, model behavior, and regression trends in real time.
• Generate regular quality reports summarizing findings, trends, and recommended improvements.

Cross-Platform Integration Testing

• Validate AI integration across multiple layers — from Android OS to application and cloud endpoints — ensuring seamless user experiences.
• Collaborate with platform, ML, and app teams to verify feature readiness and consistency across devices and environments.
• Ensure compatibility and coherence across software versions, model updates, and hardware configurations.

Collaboration & Communication

• Create clear test plans, documentation, and SOPs to maintain consistency and scalability of testing processes.
• Partner closely with project managers, developers, and ML engineers to improve AI quality evaluation practices.
• Contribute to strategy discussions and help shape the long-term testing roadmap for cross-system AI features.

Required Qualifications

• Strong proficiency in C++, Java, and Python, with proven experience in test development and system-level debugging.
• Skilled in shell scripting and SQL querying, with ability to automate workflows and perform data analysis independently.
• Experience with Android system debugging tools (ADB, logcat, tombstone) and troubleshooting across native and app layers.
• Familiar with CI/CD tools (e.g., Jenkins, GitLab CI) and Git-based version control and review processes.
• Understanding of Android system architecture, including AOSP, HAL, JNI, and AIDL.
• Hands-on experience testing AI or ML-based systems, including dataset validation and metric tracking.
• Data analysis and visualization proficiency — capable of building dashboards to monitor quality signals.
• Excellent communication and documentation skills; fluent in English and effective in global, cross-functional teams.

Preferred Qualifications

• Experience with Tradefed, NNAPI, TensorFlow Lite, or other Android AI frameworks.
• Skilled in data warehousing, cron job automation, log parsing, and metric tracking.
• Familiar with agile environments and large-scale global collaboration.
• Prior exposure to testing multi-device or distributed AI systems (mobile, web, backend, or embedded).

Why This Role?

• Impact at Scale: Your work will directly enhance the intelligence, speed, and reliability of AI experiences used by billions.
• Technical Depth: Work across OS, app, and AI system layers — from deep debugging to end-user experience validation.
• Continuous Innovation: Collaborate with world-class teams pushing the limits of platform intelligence and on-device AI.
• Visibility & Growth: Contribute to a flagship AI initiative at the intersection of systems engineering, data analysis, and automation excellence.