Machine Learning Engineer Jobs at Apple with Visa Sponsorship
Apple's Machine Learning Engineer roles sit at the intersection of research and product, spanning on-device intelligence, neural engine optimization, and large-scale ML infrastructure. Apple has a consistent track record of sponsoring international engineers across multiple visa categories, making it one of the more accessible paths for qualified ML professionals.
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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 Machine Learning Engineer Jobs at Apple Jobs
Align your portfolio to Apple's ML stack
Apple prioritizes on-device inference, Core ML, and privacy-preserving machine learning over cloud-first architectures. Tailor your GitHub projects and technical writeups to reflect these priorities before applying, since recruiters screen for this alignment early.
Target teams with active LCA filings
Apple's ML hiring spans Siri, Vision Pro, Health, and Silicon teams. Search DOL's FLAG portal for Apple LCA filings filtered to machine learning job titles to identify which teams are actively sponsoring and what prevailing wage tiers they're filing under.
Prepare for Apple's multi-round ML system design interviews
Apple's ML engineer loop typically includes a coding screen, an ML system design round, and a domain-specific deep dive. Practicing end-to-end model deployment scenarios, not just algorithm questions, reflects the production focus Apple's teams expect.
Use Migrate Mate to filter Apple ML roles by visa type
Apple posts Machine Learning Engineer roles across multiple portals, but not all listings surface sponsorship details. Use Migrate Mate to filter Apple's open ML positions by the visa categories you're eligible for, so you're applying to roles where your status is already a fit.
Machine Learning Engineer at Apple jobs are hiring across the US. Find yours.
Find Machine Learning Engineer at Apple JobsFrequently Asked Questions
Does Apple sponsor H-1B visas for Machine Learning Engineers?
Yes, Apple sponsors H-1B visas for Machine Learning Engineers. The process requires Apple to file a Labor Condition Application with the DOL and then a petition with USCIS. If you're subject to the H-1B cap, your start date is tied to the lottery, which runs annually in March. Apple also sponsors H-1B transfers, so if you already hold H-1B status with another employer, you can begin working at Apple once USCIS receives the transfer petition.
How do I apply for Machine Learning Engineer jobs at Apple?
Apply directly through Apple's careers portal and tailor your resume to the specific team, since roles across Siri, Health AI, and Silicon have different technical expectations. Referrals from current Apple engineers carry weight in their screening process. You can also browse open Machine Learning Engineer positions at Apple filtered by visa type on Migrate Mate, which helps you identify roles where sponsorship is already part of the hiring plan.
Which visa types does Apple commonly use for Machine Learning Engineers?
Apple sponsors H-1B, H-1B1, E-3, TN, EB-2, EB-3, F-1 OPT, and F-1 CPT for Machine Learning Engineer roles. E-3 is available to Australian citizens, TN to Canadian and Mexican nationals, and H-1B1 to Chilean and Singaporean nationals. These non-lottery visa categories are often processed faster than H-1B cap-subject petitions, which can affect your negotiated start date.
What qualifications does Apple expect for Machine Learning Engineer roles?
Apple's ML engineer roles generally require a graduate degree in computer science, electrical engineering, or a closely related field, with strong foundations in statistics, linear algebra, and model optimization. Hands-on experience with production ML systems matters more than research publications alone. For specialty occupation visa purposes, your degree field needs to align directly with the ML subfield the role targets, whether that's computer vision, NLP, or on-device inference.
How long does the visa sponsorship process take for Apple ML roles?
Timeline depends on your visa category. E-3 and TN can move quickly, often within a few weeks of offer acceptance if your documentation is in order. H-1B cap-subject cases are tied to the annual lottery, meaning a March registration and an October 1 start date at the earliest. USCIS premium processing is available for H-1B petitions and reduces the adjudication window to 15 business days, which Apple's immigration team often uses to accelerate timelines.
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