ML Data - Research Scientist
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Description
When collecting multi-modal human data, establishing a reliable "ground truth" can be notoriously difficult and subjective. We are looking for an ML Data Researcher who will tackle this exact challenge. Your goal will be to design and conduct experiments on our data collection methods to discover techniques that reduce subjectivity, improve signal quality, and ultimately help our multi-modal models converge faster and achieve higher performance.
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Responsibilities:
Define research topics and data strategies aligned with model performance and product requirements
Design, build, and run controlled experiments to evaluate how data collection methods, quality, and composition affect model convergence and downstream performance
Design new ways to collect, label, and interpret multi-modal human data, translating insights into improved collection protocols and data specifications
Design novel methods to collect, analyze, and interpret multi-modal human data, translating these insights into highly reliable "ground truth" specifications
Characterize the practical and statistical limits of data collection setups (sensors, protocols, annotation pipelines) to inform feasibility and define data requirements
Develop and analyze real-world data collection experiments (e.g., A/B tests on collection methodology, sampling strategy, labeling schemes) to support model training work
Analyze and report results clearly, translating findings into actionable recommendations for data pipelines, labeling guidelines, and dataset design
Partner with multidisciplinary teams across hardware, software, research, and ML engineering to drive data collection from concept to production-scale
Preferred Qualifications
MS/PhD in Computer Science, Machine Learning, Statistics, Neuroscience, or a closely related field
10 years of relevant industry experience
Experience with multi-modal human data (e.g., video, audio, motion capture) and the challenges of establishing ground truth for such data
Familiarity with human probing/annotation methods and inter-rater reliability techniques
Experience designing data collection experiments and interpreting their effect on model training/convergence
Outstanding communicator with the ability to present complex technical findings to diverse audiences
Collaborative team player who thrives in multidisciplinary, cross-functional environment
Minimum Qualifications
BS in Computer Science, Machine Learning, Statistics, Neuroscience, or a closely related field
Hands-on experience in a data-oriented, experimentation-heavy role, ideally supporting ML model development
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Perks and Benefits
Health and Wellness
Parental Benefits
Work Flexibility
Office Life and Perks
Vacation and Time Off
Financial and Retirement
Professional Development
Diversity and Inclusion
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