below marketmost similar roles pay hereabove market
This role pays less than
54%
of similar roles. Most pay
$149,500–$241,750
— the blue band above.
At the midpoint, this role pays about
$188k
versus about
$196k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3497 open roles
on FindRole.
Listed pay typically runs
$174,149–$277,600
across 2702 roles with salary data.
The ML Data Operations Engineer joins the ML Data Operations group to support internal data collection efforts for next-generation consumer machine learning features. Working alongside scientists and engineers, this role involves the hands-on bring-up, execution, and quality oversight of internal data collection studies. Key responsibilities include planning and tracking studies, maintaining pre-release hardware and software platforms, performing troubleshooting, and creating technical documentation for study protocols. The engineer must develop a technical understanding of model objectives, data requirements, labeling, and evaluation criteria to ensure dataset quality. Required skills include experience in human user studies, behavioral research, or data collection operations. Candidates should demonstrate familiarity with ML data pipelines, annotation tools, dataset management practices, and handling sensitive human data while adhering to strict privacy and consent protocols.
Plan, execute, and track internal ML data collection studies in collaboration with researchers and engineers.
Develop a technical understanding of ML experiments, including model objectives, data requirements, and evaluation criteria.
Bring up and maintain pre-release hardware and software platforms while performing hands-on troubleshooting and triage.
Create and maintain technical documentation for platform setups, study protocols, and data handling procedures.
Manage day-to-day logistics for internal study sessions, including participant scheduling and hardware configuration.
Validate data collection requirements with algorithm, infrastructure, and hardware teams before and during execution.
Track and communicate study progress, blockers, and dataset status to cross-functional partners and senior stakeholders.
Identify workflow gaps and take initiative to define and document process improvements.
What we're looking for
Bachelor’s degree in HCI, Cognitive Science, Psychology, Engineering, Operations, or an equivalent combination of education and 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.
3+ years of experience in user research operations, data collection coordination, or a related technical operations role (preferred).
Hands-on familiarity with ML data pipelines, annotation tools, or dataset management practices (preferred).
Experience working directly with engineering and science teams with comfort reading technical documentation, data schemas, or experiment specifications (preferred).
Familiarity with handling sensitive human data and adhering to strict privacy and consent protocols (preferred).
Strong interpersonal and written communication skills with the ability to collaborate across technical and non-technical stakeholders (preferred).
Machine Learning
Data Pipelines
Data Collection
Dataset Management
Annotation Tools
Data Schemas
Software Platform Setup
Technical Documentation
Human User Studies
Machine Learning
Data Pipelines
Data Collection
Dataset Management
Annotation Tools
Data Schemas
Software Platforms
Technical Documentation
Human User Studies
Machine Learning
Data Pipelines
Data Collection
Annotation Tools
Dataset Management
Data Schemas
Software Platforms
Technical Documentation
Human User Studies
Machine Learning
ML Data Pipelines
Data Collection
Dataset Management
Data Schemas
Software Platform Setup
Technical Documentation
Human User Studies
Behavioral Research