ML Data Operations Engineer

Apple Inc

Confirmed live today Trusted

Quick summary

Work type
On-site
Location
San Diego, CA
Salary
$175,000–$308,500 / yr
Posted
23 days ago
Freshness
Confirmed live today

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $196k
This role $242k
$130k $328k
below market most similar roles pay here above market

This role pays more than 78% of similar roles. Most pay $149,500–$241,750 — the blue band above. At the midpoint, this role pays about $242k 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.

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At a glance

TL;DR · ML Data Operations Engineer

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. Preferred qualifications include familiarity with ML data pipelines, annotation tools, dataset management practices, and handling sensitive human data while adhering to strict privacy and consent protocols.

What you'll do

  • 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 labeling criteria.
  • Bring up and maintain pre-release hardware and software platforms while performing hands-on troubleshooting.
  • 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 senior stakeholders.
  • Identify workflow gaps and define, document, and socialize 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.
  • 10 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).

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