ML Data Operations Engineer

Summary

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.

Minimum Qualifications

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.

Preferred Qualifications

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.

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

Responsibilities

Plan, execute, and track internal ML data collection studies in close collaboration with researchers, engineers, and scientists across the organization Develop a working understanding of the ML experiments being supported, including model objectives, data requirements, labeling, and evaluation criteria, to ensure datasets quality Bring up and maintain pre-release hardware and software platforms used for data collection, performing hands-on troubleshooting and triage to minimize study disruptions Create and maintain clear technical documentation for hardware/software platform setup, study protocols, and data handling procedures Manage day-to-day logistics of internal study sessions, including scheduling participants, configuring hardware and software setups, and maintaining smooth session flow Collaborate with algorithm, infrastructure, and hardware/software teams to gather and validate data collection requirements before and during study execution Track and communicate study progress, blockers, participant throughput, and dataset status to cross-functional partners and senior stakeholders Identify gaps in existing workflows and take initiative to define, document, and socialize process improvements