ML Data Operations Engineer

Quick Facts

• Role: ML Data Operations Engineer
• Focus: Internal ML data collection studies for consumer machine learning features

Description

Apple’s ML Data Operations group is seeking a Data Operations Engineer to support internal data collection efforts powering next-generation consumer machine learning features. You will collaborate with Apple scientists and engineers, bringing up, executing, and overseeing the quality of internal data collection studies. The work includes both technical platform troubleshooting and coordinating study logistics.

Responsibilities

• Plan, execute, and track internal ML data collection studies with researchers, engineers, and scientists
• Understand supported ML experiments (model objectives, data requirements, labeling, evaluation criteria) to ensure dataset quality
• Bring up and maintain pre-release hardware and software platforms for data collection; perform hands-on troubleshooting and triage
• Create and maintain technical documentation for platform setup, study protocols, and data handling procedures
• Manage day-to-day logistics for internal study sessions (scheduling participants, configuring setups, maintaining session flow)
• Collaborate with algorithm, infrastructure, and hardware/software teams to gather and validate data collection requirements
• Track and communicate study progress, blockers, participant throughput, and dataset status
• Identify workflow gaps and drive process improvements by defining, documenting, and socializing changes

Requirements

• 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 academic or industry settings
• Track record of partnering with ML engineers or researchers to define data requirements, quality standards, or collection specifications

Benefits

• Work in a highly collaborative, technically demanding environment with ML scientists and engineers to deliver consumer ML features