ML Data Operations Engineer
Role details
Job location
Tech stack
Job description
Apple's ML Data Operations group is seeking a Data Operations Engineer to support internal data collection efforts powering our next generation of consumer machine learning features. In this role, you will work shoulder-to-shoulder with full-time Apple scientists and engineers, not just coordinating logistics, but developing a genuine technical understanding of the ML experiments you support. You will be responsible for the hands-on bring-up, execution, and quality oversight of internal data collection studies, operating in a highly collaborative and technically demanding cross-functional environment
Requirements
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., * Bachelor's degree in HCI, Cognitive Science, Psychology, Engineering, Operations, or equivalent combination of education and relevant experience.
- Experience supporting or executing human user studies, behavioral research, or data collection operations in an academic or industry setting.
- Track record of partnering with ML engineers or researchers to define data requirements, quality standards, or collection specifications., * 10 years of experience in user research operations, data collection coordination, or a related technical operations role.
- Hands-on familiarity with ML data pipelines, annotation tools, or dataset management practices.
- Experience working directly with engineering and science teams, with comfort reading technical documentation, data schemas, or experiment specifications.
- Familiarity with handling sensitive human data and adhering to strict privacy and consent protocols.
- Highly organized self-starter who can manage multiple concurrent internal studies with minimal oversight.
- Strong interpersonal and written communication skills, with the ability to collaborate fluidly across both technical and non-technical stakeholders.
- Strong attention to detail with the ability to identify data anomalies and inconsistencies during live collection.