About the role

What will you do at Apple?

Imagine what you could do here. At Apple, new ideas have a way of becoming

outstanding products, services, and customer experiences very quickly. Bring

passion and dedication to your job and there's no telling what you could

accomplish. The Creativity Apps QA team is seeking a Software QA Engineer,

Machine Learning to help us ensure that our iOS and macOS creativity

applications are high-quality. We’re looking for an engineer to take projects

from defining evaluation strategy, evaluation scripting and automation, leading

all stages of testing, triaging and driving issues to resolution, and ensuring

high quality models are integrated into our Apps' clients through rigorous

testing methodologies. You will have the unique and rewarding opportunity to

help shape upcoming products that will delight and inspire millions of Apple’s

customers every day.

DESCRIPTION

This position requires a self-motivated individual with strong technical,

problem solving and communication skills, and the willingness to work in a

sophisticated environment.

Responsibilities

include scripting, parsing data, and
working closely with the annotation operations team. Analyzing annotations and
test results to ensure the features work end-to-end for customers. Developing
and implementing workflow projects, regression testing, and bug filing. You will
work closely with other team members to develop requirements, identify
appropriate testing implementations processes, and methodologies. You should be
able to be comfortable with ambiguity and make educated, critical judgments. You
must be effective and systematic at managing priorities, and communicating
progress as well as risk through regular status updates. Have a proven ability
to ramp up quickly on both existing and new technologies.
MINIMUM QUALIFICATIONS
At least 5 years industry experience ideally in quality assurance with a prior
focus in Machine Learning. Strong comprehension of machine learning algorithms,
supervised and unsupervised modeling techniques. Understand of the mechanics of
machine learning, deep learning, computer vision, natural language processing,
and generative AI. Proficient in Python and/or Swift or comparable languages.
Preparing and collecting data for evaluation and ensuring data quality.
Meticulous, analytical, methodical and creative problem solving ability with a
commitment to driving quality forward. Detail-oriented, analytical, methodical
and creative thinker committed to driving quality forward. Prototyping and tools
development experience. Strong presentation skills and ability to adapt
technical presentation to various audiences. Proficiency in utilizing LLMs and
Generative AI frameworks to automate model evaluation processes, optimize test
execution times, and improve evaluation accuracy and efficiency. Bachelor's
degree in Computer Science, Data Science, Applied Mathematics or similar,
combined with an understanding of SQA methodologies, machine learning and
software engineering experience.
PREFERRED QUALIFICATIONS
Understanding of subjective/objective image and video quality evaluation, and
color science concepts is a strong plus. Understanding of SQA methodologies and
practices, software engineering experience preferred. iOS and macOS familiarity.

Which skills does this role require?

Machine LearningSoftware QAPythonSwiftDeep LearningComputer VisionNatural Language ProcessingGenerative AILLMsData AnalysisRegression TestingAutomationPrototypingModel EvaluationBug FilingTechnical CommunicationData QualityComputer ScienceData ScienceApplied MathematicsAnnotationBug Tracking

Make your next move

Build a shortlist and prepare

Identify the requirements you can demonstrate, then choose examples from your work to discuss with the hiring team.

Build a focused shortlist before you applyCompare role requirements with your experience and give each application a clear reason.
Machine learning jobsCompare current openings and review what to look for in this role.
Practice explaining your experience in an interviewRehearse your answers before meeting the hiring team.

Review the responsibilities and requirements before adding an opening to your shortlist.