ML Data - Research Scientist

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, smart people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same passion for innovation that goes into our products also applies to our practices, strengthening our commitment to leave the world better than we found it. Join us to help deliver the next groundbreaking Apple product. Do you love working on challenges that no one has solved yet? As a member of our dynamic group, you will have the unique and rewarding opportunity to craft upcoming products that will delight and inspire millions of Apple's customers every single day.

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.

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

Preferred Qualifications

MS/PhD in Computer Science, Machine Learning, Statistics, Neuroscience, or a closely related field
3 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