ML Data Operations Engineer: Drive Internal Data Studies

Apple Inc.; California; On-site; USD 150,000 - 225,000; Full time

Benefits offered by this job

Stock programs

Restricted Stock Units

Employee Stock Purchase Plan

Medical and dental coverage

Tuition reimbursement

Relocation assistance

Job summary

Apple Inc. in Cupertino, California, is seeking a Data Operations Engineer to support internal data collection for ML experiments. You will work closely with scientists and engineers, bringing up hardware and software platforms and ensuring data quality.

The role emphasizes hands-on setup, documentation, and cross-functional collaboration across ML, infrastructure, and hardware teams to run studies efficiently.

Qualifications

• Bachelor’s degree in HCI, Cognitive Science, Psychology, Engineering, Operations or equivalent.
• Experience supporting human user studies, data collection operations, or related research.
• Track record partnering with ML engineers or researchers to define data needs and quality standards.

Responsibilities

• Plan, execute and track internal ML data collection studies with researchers and engineers.
• Develop understanding of ML experiments, data requirements and evaluation criteria.
• Set up and maintain pre-release hardware/software platforms for data collection.
• Create clear technical documentation for setup, study protocols and data handling.
• Manage day-to-day logistics of study sessions and participant configurations.
• Collaborate with ML, infrastructure and hardware teams to validate data needs.
• Track study progress and communicate blockers to cross‑functional partners.
• Identify gaps and propose process improvements.

Skills

Data operations

Cross-functional collaboration

Study coordination

Education

Bachelor’s degree in HCI, Cognitive Science, Psychology, Engineering, Operations

Tools

ML data pipelines

Annotation tools

Dataset management

Job description

Apple Inc. in Cupertino, California, is seeking a Data Operations Engineer to support internal data collection for ML experiments. You will work closely with scientists and engineers, bringing up hardware and software platforms and ensuring data quality.

The role emphasizes hands-on setup, documentation, and cross-functional collaboration across ML, infrastructure, and hardware teams to run studies efficiently.