Home Product Systems Quality Engineer

We are the Apple Home Product Systems Quality team, and we are looking for a highly motivated and experienced QA engineer with a passion for delivering robust, state-of-the-art validation for Apple's next generation of products — like Apple HomeView’s recognition experience. You'll be responsible for functional testing in your ownership area, while partnering closely with Data Science teammates to plan, execute, and iterate on both feature-level tests and advanced algorithm small user studies that directly influence hardware and model design and performance.

Come be a part of our team and use both creativity and technical expertise to bring experiences to life that our customers love!

We are seeking an experienced Systems Quality Engineer to own end-to-end validation of hardware, software, and machine learning features for Apple's Home products. This role bridges functional testing, user studies, and algorithm performance—solving problems at the intersection of system design, real-world constraints, and the customer experience.

Minimum Qualifications

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or related field, with a minimum of 3 years in an end user quality-focused role.
Good understanding of QA methodologies, such as test planning, scheduling, and regression.
Strong programming/scripting skills with hands-on experience in Python or equivalent language.
Experience testing products utilizing computer vision, machine learning, or related areas.
Experience being accountable for the quality of a technical area and demonstrated understanding of how it works, working with partners across different teams and organizations to clearly communicate status and raise critical issues.
Ability to think analytically, reduce data sets, problem-solve, and innovate.

Preferred Qualifications

5 or more years of industry experience in a quality-focused role, preferably working on consumer electronics or connected/smart home devices.
Strong background in statistical experimental design and hypothesis testing, applied to interpreting system-level test results.
Experience managing the operational side of participant studies — recruiting, consent and privacy protocols, and scheduling logistics — for both small-scale and larger-scale collections.
Data analysis, visualization, and reporting experience with tools such as Tableau.
Understanding of how to test and quantify performance of sensing technologies such as camera, IMU, capacitive, environmental, light, motion, radar, optical, acoustic, and evaluate user impact and performance.