Machine Learning Jobs at Apple with Visa Sponsorship
Apple's Machine Learning teams build the models powering Siri, on-device inference, computer vision, and neural engine optimization across its entire product ecosystem. Apple has a consistent track record of sponsoring work visas for ML engineers and researchers, covering multiple visa categories from OPT through permanent residence.
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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 Jobs at Apple Jobs
Tailor your ML portfolio to Apple's stack
Apple prioritizes on-device inference, Core ML, and privacy-preserving machine learning. Showcase projects involving model compression, neural architecture search, or federated learning rather than cloud-scale training work more relevant to other employers.
Target teams publishing Apple research
Apple's ML Research blog and publications on arXiv signal which teams are actively hiring. Applying to roles whose published work aligns with your specialization puts you in front of hiring managers with an existing research agenda that matches your background.
Understand which visa fits your citizenship
Australian citizens can pursue the E-3 visa, which has no lottery and allows year-round filing, while Canadians may qualify for TN status. Knowing which category applies before your offer stage prevents delays when Apple's legal team initiates the petition.
Prepare for a multi-round technical process
Apple's ML interviews typically include coding screens, ML system design rounds, and a domain-specific research discussion. Having your GitHub, publications, and documented project outcomes ready before the recruiter screen shortens the credentialing review Apple's immigration team conducts post-offer.
Use Migrate Mate to find open ML roles at Apple
Apple posts Machine Learning roles across teams simultaneously, and openings move quickly. Search and filter active Apple ML positions by visa type and team focus on Migrate Mate to identify roles aligned with your specialization before they close.
Machine Learning at Apple jobs are hiring across the US. Find yours.
Find Machine Learning at Apple JobsFrequently Asked Questions
Does Apple sponsor H-1B visas for Machine Learning roles?
Yes, Apple sponsors H-1B visas for Machine Learning engineers and researchers. Apple participates in the annual H-1B cap lottery each April for candidates without existing H-1B status, and can also file for cap-exempt transfers if you already hold an approved H-1B petition from a previous employer. Apple's immigration team manages the full filing process after an offer is accepted.
How do I apply for Machine Learning jobs at Apple?
Applications go through Apple's careers portal at jobs.apple.com, where ML roles are listed by team, such as Siri, Core ML, or Health AI. You can also browse and filter open Apple Machine Learning positions by visa type on Migrate Mate, which surfaces roles actively open to sponsored candidates. Tailoring your resume to Apple's on-device ML focus improves your chances of passing initial screening.
Which visa types does Apple commonly use for Machine Learning roles?
Apple sponsors H-1B and H-1B1 visas for ML roles, along with E-3 visas for Australian citizens and TN status for Canadian and Mexican nationals. F-1 OPT and CPT are accepted for students and recent graduates, and Apple files EB-2 and EB-3 immigrant visa petitions, including PERM labor certifications, for employees pursuing permanent residence.
What qualifications does Apple expect for sponsored Machine Learning positions?
Most Apple ML roles require a Bachelor's degree at minimum in Computer Science, Electrical Engineering, or a related field, with many research and senior roles preferring a Master's or PhD. Hands-on experience with PyTorch or JAX, familiarity with model optimization for edge deployment, and a track record of shipping ML features in production environments are consistently emphasized across Apple job descriptions for this function.
How do I time my application around the H-1B cap and OPT expiration?
USCIS opens H-1B registrations in March each year, with cap-subject petitions taking effect October 1 if selected. If your OPT expires before October 1, Apple can file a cap-gap extension to bridge the gap as long as your H-1B petition is pending. Starting your job search no later than December or January gives enough runway to receive an offer, complete Apple's internal review, and register before the March window closes.
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