ML Data Operations Engineer

Socket.dev; Santa Clara (CA); On-site; USD 120,000 - 170,000; Full time

Job summary

Apple in Santa Clara, CA is seeking a Data Operations Engineer to support internal data collection efforts powering our next generation of consumer machine learning features. 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,

Qualifications

• Bachelor's degree in HCI, Cognitive Science, Psychology, Engineering, Operations, or equivalent.
• Experience supporting or executing human user studies or data collection operations in academic or industry settings.
• Partner with ML engineers to define data requirements, quality standards, or collection specifications.

Responsibilities

• Hands-on bring-up, execution, and quality oversight of internal data collection studies.
• Collaborate with ML scientists and engineers in a cross-functional environment.

Skills

User research operations

Data collection coordination

Cross-functional collaboration

Education

Bachelor's degree in HCI / Cognitive Science / Psychology / Engineering / Operations

Tools

ML data pipelines

Annotation tools

Dataset management

Job description

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

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

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

• 3+ 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.