{"id":3726823,"url":"https://alion.io/job/apple-ml-data-operations-engineer-ai-assisted-annotation","title":"ML Data Operations Engineer, AI-Assisted Annotation","company":{"id":12,"name":"Apple","domain":"apple.com","url":"https://alion.io/company/apple","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Apple Jobs","truth_index":{"grade":"A","score":89,"open_postings":812,"ghost_share":0,"stale_share":0.222,"repost_share":0.004,"time_to_fill_p50_days":90,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Backend","role_family":"Backend","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Diego, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"SQL","optional":false},{"name":"Computer Vision","optional":true},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-11-04T01:02:47Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-08T03:11:49Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"The Gatos team is a cross-discipline production and research data annotation team within Video Engineering. We deliver the labeled data that algorithm teams use to build and evaluate features for millions of devices and users around the world. We're looking for an operations engineer who will own annotation projects from the first request to delivered data. That includes a growing set of AI-assisted and agent-based workflows, where models do much of the labeling and people guide, review and verify the results.\nDescription\nIn this role you will:\nWork with algorithm teams to define what success looks like for each data request, and translate it into a clear task specification, labeling guidelines and a quality bar.\nOwn annotation projects end to end, from pilot through production to delivery, and be accountable for quality, schedule and the usefulness of the data to the customer.\nDesign, implement, and run AI-assisted labeling workflows: models pre-label, automated steps handle routine work, and people focus on judgment and verification. Measure the impact on quality, throughput and cost.\nSet acceptance criteria, run quality checks and audits, analyze error patterns, and close the loop with guideline owners and labeling teams.\nTrack project status and risks, and raise issues early with a clear ask and a root cause.\nCollaborate with data scientists, software and infrastructure engineers, data collection teams, business operations, and external labeling teams across time zones.\nImprove how the team works by building and sharing tools and workflows that use AI.\nMinimum Qualifications\nBS and a minimum of 3 years relevant industry experience.\nExperience delivering data for machine learning, such as data annotation, data operations, or technical program management.\nExperience defining annotation tasks, labeling guidelines, and quality metrics.\nExperience serving as the accountable owner for projects that span multiple teams, from ambiguous requirements to delivery.\nExperience working hands-on with data and technical tools, such as writing basic SQL queries or scripts, analyzing data in spreadsheets, or configuring labeling platforms.\nPreferred Qualifications\nExperience with model-assisted labeling, pseudo-labeling, or agent-based workflows, including judging where people must stay in the loop.\nExperience with Python or SQL for data analysis and automation.\nFamiliarity with computer vision data such as images, video, depth, or 3D.\nExperience building reports or dashboards for cross-functional partners.\nClear written and verbal communication, including status updates and escalations to senior leaders.\nWorks effectively across time zones with internal teams and external partners.\nDefines success with customers before starting the work, and adapts as their needs 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