AI Quality Analyst

Zebra Technologies Corporation•Lincolnshire, IL

14h•Hybrid

About The Position

The AI Quality Analyst is a critical role responsible for ensuring the performance, safety, and reliability of our cutting-edge AI/ML models. You will be at the forefront of our development lifecycle, designing and executing comprehensive evaluation strategies to identify model weaknesses, potential biases, and critical edge cases. This role requires a blend of analytical rigor, technical aptitude, and a deep curiosity for how AI models behave in real-world scenarios. You will not just find bugs, but provide the actionable insights that drive model improvement and guide our research and development efforts.

Requirements

Proven experience in a quality assurance, testing, or data analysis role, preferably within the AI/ML domain.
A deep understanding of the machine learning lifecycle and the common failure modes of AI models.
Hands-on experience with data annotation, data validation, and managing large datasets.
Meticulous attention to detail and a methodical approach to problem-solving.
Strong analytical skills with the ability to identify patterns in data and draw meaningful conclusions.
Expertise with industry-standard test automation tools and libraries (e.g., Selenium, Playwright, Cypress, REST-assured).
Experience in testing across different platforms (e.g. comprehensive testing of mobile Android/iOS and web applications)
Experience with bug tracking systems (e.g., Jira) and test case management tools.
Scripting skills (e.g., Python) for test automation and data manipulation.
Excellent communication skills, with the ability to clearly document bugs and articulate complex technical issues.

Nice To Haves

(Preferred) experience in testing AI systems, including evaluating agentic responses, model performance metrics, and data integrity.
Familiarity with computer vision or other specific AI domains relevant to our work.

Responsibilities

Design, develop, and maintain a comprehensive suite of test cases and evaluation benchmarks.
Proactively identify potential model failure points, including edge cases, adversarial inputs, and sources of bias.
Conduct systematic error analysis to categorize model failures and identify underlying patterns.
Triage defects, prioritize them based on severity and impact, and work with the development team to ensure resolution.
Source, curate, and manage high-quality datasets for model evaluation and testing. This includes performing data annotation and validation to ensure the integrity of our ground-truth data.
Perform unscripted, exploratory testing to discover unexpected model behaviors.
Participate in red teaming exercises to intentionally challenge our models and identify potential safety and security vulnerabilities.
Set up, maintain, and troubleshoot testing and demonstration environments to ensure a stable and reliable evaluation pipeline.
Analyze and synthesize test results into clear, actionable reports for both technical and non-technical stakeholders.
Translate complex findings into concrete recommendations for model improvement.
Actively participate in post-hoc evaluation reviews and contribute to the continuous improvement of our testing methodologies, tools, and overall quality assurance processes.