Analytics Engineer Jobs at Apple with Visa Sponsorship
Analytics Engineer roles at Apple sit at the intersection of data infrastructure and product intelligence, supporting teams that build and scale some of the world's most used consumer technology. Apple has a strong track record of sponsoring international talent for this function across multiple visa categories, making it a realistic target for qualified candidates.
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INTRODUCTION
The Productivity and Machine Learning Evaluation team ensures the quality of AI-powered features across a suite of productivity and creative applications; including Creator Studio, used by hundreds of millions of people. This team serves as the primary evaluation function, and its analysis directly informs decisions about model development, feature launches, and product direction.
This role is the analytical core of the team; responsible for making sense of evaluation signals and real-world user behavior. The work involves designing feature-level quality metrics, collaborating with partner teams on data collection strategies, and translating evaluation data into concise, actionable insights that drive decisions. This is an opportunity to define how AI feature quality is measured and to directly shape what gets shipped. As AI features evolve into multi-turn, agentic experiences, this role will define what “quality” means when the unit of evaluation is a conversation, not a single response.
DESCRIPTION
Day-to-day work involves analyzing evaluation results, identifying trends, regressions, and segment-level patterns across multiple AI features. This includes collaborating with partner teams on data collection strategies, ensuring evaluation data is representative of real-world usage, and designing the metrics framework that leadership uses to make decisions on AI features.
Typical deliverables include: feature-level quality metrics and dashboards, evaluation analysis reports, data collection requirements, dataset representativeness audits, multi-turn evaluation frameworks and session-level scoring rubrics, and concise metric summaries for decision-makers.
Responsibilities
- Define and own the quality metrics framework across AI features and agentic experiences, ensuring each feature has a clear north-star metric and supporting diagnostics
- Analyze evaluation outputs to identify quality trends, regressions, and segment-level patterns across both single-turn and multi-turn interactions, tracking how quality degrades or holds over extended conversations
- Drive the data collection strategy with partner teams
- Ensure evaluation data stays grounded in real-world user behavior
- Audit evaluation data representativeness to verify that datasets reflect actual user distributions
- Assess alignment across different evaluation methods, identifying where they agree, diverge, and why
- Deliver concise, decision-ready metric summaries to leadership, translating detailed analysis into clear quality assessments and recommendations
- Influence model development direction by providing actionable feedback on specific failure patterns and data gaps
MINIMUM QUALIFICATIONS
- Bachelor’s degree in Statistics, Data Science, Applied Mathematics, Computer Science, or a related quantitative field
- 5+ years of experience in applied science, data science, or evaluation research, with a focus on defining and operationalizing quality metrics
- Experience with statistical analysis methods including significance testing, sampling design, effect size estimation, and experimental design
- Experience working with production user data, understanding its biases and limitations compared to controlled evaluation data, including familiarity with sequential interaction data where context and turn order affect quality assessment
- Ability to design evaluation approaches where the unit of analysis is a session or conversation rather than a single model output
- Track record of independently designing metrics frameworks and driving data-informed decisions across cross-functional teams
- Proficiency in Python (pandas, scipy, scikit-learn) or R for data analysis and visualization
PREFERRED QUALIFICATIONS
- Experience designing evaluation or quality metrics for AI-powered or ML-driven features in consumer-facing products
- Familiarity with productivity software or creative applications, with an ability to distinguish between technically correct and genuinely useful AI outputs
- Experience partnering with engineering or data teams to define data collection requirements and schemas
- Track record of translating complex analytical findings into concise recommendations for non-technical decision-makers
- Experience evaluating tool-use accuracy, retrieval quality, or function-calling reliability within AI systems
- Experience with evaluation methodology including inter-annotator agreement, evaluation bias detection, and dataset representativeness auditing
- Familiarity with agentic orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen) and emerging agent interoperability protocols (A2A, MCP), with an understanding of how architectural choices in agent design affect evaluability
- Understanding of ML model development processes, with the ability to specify what evaluation signals are useful for model improvement
- Experience managing evaluation across multiple features or product areas simultaneously, with systematic rather than ad-hoc approaches
- Graduate degree in a relevant quantitative field
PAY & BENEFITS
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $139,500 and $258,100, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

INTRODUCTION
The Productivity and Machine Learning Evaluation team ensures the quality of AI-powered features across a suite of productivity and creative applications; including Creator Studio, used by hundreds of millions of people. This team serves as the primary evaluation function, and its analysis directly informs decisions about model development, feature launches, and product direction.
This role is the analytical core of the team; responsible for making sense of evaluation signals and real-world user behavior. The work involves designing feature-level quality metrics, collaborating with partner teams on data collection strategies, and translating evaluation data into concise, actionable insights that drive decisions. This is an opportunity to define how AI feature quality is measured and to directly shape what gets shipped. As AI features evolve into multi-turn, agentic experiences, this role will define what “quality” means when the unit of evaluation is a conversation, not a single response.
DESCRIPTION
Day-to-day work involves analyzing evaluation results, identifying trends, regressions, and segment-level patterns across multiple AI features. This includes collaborating with partner teams on data collection strategies, ensuring evaluation data is representative of real-world usage, and designing the metrics framework that leadership uses to make decisions on AI features.
Typical deliverables include: feature-level quality metrics and dashboards, evaluation analysis reports, data collection requirements, dataset representativeness audits, multi-turn evaluation frameworks and session-level scoring rubrics, and concise metric summaries for decision-makers.
Responsibilities
- Define and own the quality metrics framework across AI features and agentic experiences, ensuring each feature has a clear north-star metric and supporting diagnostics
- Analyze evaluation outputs to identify quality trends, regressions, and segment-level patterns across both single-turn and multi-turn interactions, tracking how quality degrades or holds over extended conversations
- Drive the data collection strategy with partner teams
- Ensure evaluation data stays grounded in real-world user behavior
- Audit evaluation data representativeness to verify that datasets reflect actual user distributions
- Assess alignment across different evaluation methods, identifying where they agree, diverge, and why
- Deliver concise, decision-ready metric summaries to leadership, translating detailed analysis into clear quality assessments and recommendations
- Influence model development direction by providing actionable feedback on specific failure patterns and data gaps
MINIMUM QUALIFICATIONS
- Bachelor’s degree in Statistics, Data Science, Applied Mathematics, Computer Science, or a related quantitative field
- 5+ years of experience in applied science, data science, or evaluation research, with a focus on defining and operationalizing quality metrics
- Experience with statistical analysis methods including significance testing, sampling design, effect size estimation, and experimental design
- Experience working with production user data, understanding its biases and limitations compared to controlled evaluation data, including familiarity with sequential interaction data where context and turn order affect quality assessment
- Ability to design evaluation approaches where the unit of analysis is a session or conversation rather than a single model output
- Track record of independently designing metrics frameworks and driving data-informed decisions across cross-functional teams
- Proficiency in Python (pandas, scipy, scikit-learn) or R for data analysis and visualization
PREFERRED QUALIFICATIONS
- Experience designing evaluation or quality metrics for AI-powered or ML-driven features in consumer-facing products
- Familiarity with productivity software or creative applications, with an ability to distinguish between technically correct and genuinely useful AI outputs
- Experience partnering with engineering or data teams to define data collection requirements and schemas
- Track record of translating complex analytical findings into concise recommendations for non-technical decision-makers
- Experience evaluating tool-use accuracy, retrieval quality, or function-calling reliability within AI systems
- Experience with evaluation methodology including inter-annotator agreement, evaluation bias detection, and dataset representativeness auditing
- Familiarity with agentic orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen) and emerging agent interoperability protocols (A2A, MCP), with an understanding of how architectural choices in agent design affect evaluability
- Understanding of ML model development processes, with the ability to specify what evaluation signals are useful for model improvement
- Experience managing evaluation across multiple features or product areas simultaneously, with systematic rather than ad-hoc approaches
- Graduate degree in a relevant quantitative field
PAY & BENEFITS
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $139,500 and $258,100, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
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Get Access To All JobsTips for Finding Analytics Engineer Jobs at Apple Jobs
Align your portfolio to Apple's data stack
Apple's analytics engineering teams work heavily with large-scale event data and internal tooling. Before applying, build portfolio projects that demonstrate experience with SQL, dbt, and pipeline orchestration at scale, not just dashboarding or reporting work.
Use Migrate Mate to filter Apple's open roles
Apple posts Analytics Engineer openings across several business units simultaneously. Use Migrate Mate to filter specifically for Apple roles that match your visa type, so you're applying to positions where sponsorship is already confirmed rather than guessing from a general job board.
Prepare your LCA documentation before the offer stage
Apple files a Labor Condition Application with the DOL before your H-1B petition can proceed. Understanding prevailing wage levels for your target location in advance helps you negotiate confidently and avoids surprises when the offer letter specifies a wage tier.
Front-load your technical interview with systems thinking
Apple's analytics engineering interviews weight data modeling design and systems architecture heavily. Candidates who frame past work around upstream dependencies, schema design decisions, and downstream consumer needs consistently report stronger outcomes than those focused solely on query optimization.
Time your OPT application to cover the H-1B cap gap
If you're on F-1 OPT, confirm your OPT end date against the H-1B cap-subject start date of October 1. If there's a gap, USCIS cap-gap protection may extend your work authorization automatically, but only if your employer files before your OPT expires.
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Find Analytics Engineer at Apple JobsFrequently Asked Questions
Does Apple sponsor H-1B visas for Analytics Engineers?
Yes, Apple sponsors H-1B visas for Analytics Engineer roles. Apple participates in the annual H-1B lottery each spring and files petitions for selected candidates ahead of the October 1 start date. Because Apple is a large employer, it has established internal immigration teams that manage the petition process, which typically means a more structured and predictable experience for sponsored employees than smaller companies can offer.
How do I apply for Analytics Engineer jobs at Apple?
Applications go through Apple's careers portal at apple.com/careers. Analytics Engineer roles are posted across multiple business units, including Apple Services, hardware product teams, and retail analytics. Search by role title and filter for your location. If you want to find only the roles that align with your visa type before applying, Migrate Mate lets you browse Apple's open Analytics Engineer positions filtered by sponsorship eligibility.
Which visa types does Apple commonly sponsor for Analytics Engineers?
Apple sponsors a range of visa categories for Analytics Engineer roles, including H-1B, H-1B1 for Chilean and Singaporean nationals, E-3 for Australian citizens, TN for Canadian and Mexican nationals, F-1 OPT and CPT for current students, and employment-based Green Cards through EB-2 and EB-3. The right category depends on your nationality, degree, and career stage. Apple's internal immigration team typically advises on the best pathway once you have an offer.
What qualifications does Apple look for in Analytics Engineer candidates?
Apple's Analytics Engineer postings consistently require proficiency in SQL, experience with data modeling frameworks like dbt, and familiarity with large-scale data pipelines. A bachelor's degree in computer science, statistics, or a related quantitative field is the baseline for H-1B specialty occupation requirements. Candidates with experience in consumer electronics or platform-scale data infrastructure tend to be more competitive for Apple's hardware and services business units specifically.
How does the sponsorship timeline work for an Analytics Engineer offer at Apple?
Once you have an offer, Apple's immigration team files a Labor Condition Application with the DOL, which typically certifies within seven business days. For H-1B cap-subject cases, USCIS registration opens each March and results are announced within weeks. If selected, the petition is filed by June for an October 1 start date. Premium processing is available and reduces the USCIS adjudication window to 15 business days, which Apple sometimes uses for time-sensitive hires.
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