Machine Learning Data Researcher

Apple's Hardware group is home to some of the world's most advanced technologies - powering the experiences that define Apple products. We are looking for a talented and curious ML Data Researcher to join our growing Deep Learning team in Herzliya, working with physical, real-world signal data.

Description

In this role, you will help create the full data lifecycle that underpins our deep learning models: from designing what physical signal data we collect, through curation and quality monitoring, to model training and rigorous experimentation that drive model improvements. You will work closely with other Machine Learning and Data Engineering teams to ensure our models are trained on the best possible physical signal data, reaching the best accuracy, and that we deeply understand when and why they don't perform as expected - including building the tools to explain and interpret their behavior.

Minimum Qualifications

Ms.c. in Computer Science, Electrical Engineering, Computational Biology/Neuroscience, Mathematics, Statistics, or a related field
5+ years of industry experience in applied deep learning, data science, or a related field
Hands-on experience working with physical/real-world signal data
Strong hands-on experience with Python, PyTorch and SQL for large-scale signal/waveform data analysis and pipeline development
Hands-on experience with the full deep learning experimentation cycle: problem definition, data collection, statistical analysis, and conclusion-driven iteration
Proven ability to analyze model failures and translate findings into concrete improvements
Strong analytical thinking and ability to independently define and drive research directions
Excellent cross-functional communication skills - ability to work effectively with other Deep Learning and Data Engineers

Preferred Qualifications

Experience with model explainability and interpretability methods - a strong advantage
Experience with continual or online learning - a strong advantage
Experience with data-efficient training strategies - a strong advantage
Experience with applied speech, audio, or signal processing deep learning systems
Familiarity with data quality frameworks, monitoring pipelines, and data validation at scale
Strong statistical foundation - hypothesis testing, uncertainty quantification, evaluation metrics design
Ph.D. in Computer Science, Electrical Engineering, Computational Biology/Neuroscience, Mathematics, Statistics, or a related field