ML Engineer Jobs at Apple with Visa Sponsorship
Apple's ML Engineer roles sit at the intersection of hardware and intelligence, spanning on-device inference, Core ML, and large-scale training infrastructure. Apple sponsors across multiple visa categories for this function, making it a realistic target if you're an international candidate with a strong machine learning background.
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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 ML Engineer Jobs at Apple Jobs
Align your portfolio to Apple's ML stack
Apple prioritizes on-device ML, neural engine optimization, and privacy-preserving inference. Before applying, reframe your project work around model compression, Core ML deployment, or federated learning rather than cloud-scale training pipelines.
Target teams filing Labor Condition Applications early
Apple's LCA filings with DOL reveal which teams are actively hiring internationally. Search OFLC disclosure data by employer and job title to identify which ML specializations Apple is sponsoring right now, then target those specific roles.
Front-load your USCIS petition documentation
For ML Engineer roles, Apple's immigration team will need evidence that the position requires a degree in a specific field. Gather transcripts, your degree certificate, and any credential evaluations before your offer letter arrives to avoid delays in the I-129 filing.
Use Migrate Mate to filter Apple ML Engineer openings by visa type
Sponsored ML Engineer roles at Apple don't always surface easily on general job boards. Use Migrate Mate to filter Apple's open positions by the visa category that matches your status, saving you from applying to roles where sponsorship isn't available.
Flag OPT STEM extension eligibility during the offer negotiation
If you're on F-1 OPT, Apple's size and E-Verify enrollment make it a qualifying employer for the 24-month STEM extension. Confirming this during the offer stage gives you up to three years of runway before your H-1B lottery selection becomes urgent.
ML Engineer at Apple jobs are hiring across the US. Find yours.
Find ML Engineer at Apple JobsFrequently Asked Questions
Does Apple sponsor H-1B visas for ML Engineers?
Yes, Apple sponsors H-1B visas for ML Engineers and has a well-established immigration program to support international hires in this function. Apple participates in the annual H-1B lottery each April for cap-subject candidates. If you're already in H-1B status with another employer, Apple can file an H-1B transfer, letting you start work as soon as USCIS receives the petition.
How do I apply for ML Engineer jobs at Apple?
Apply directly through Apple's careers site, targeting ML Engineer roles by team, such as Core ML, Siri, or Silicon Engineering. Tailor your resume to Apple's on-device and privacy-focused ML priorities rather than general deep learning experience. Migrate Mate lists Apple's open ML Engineer positions filtered by visa sponsorship type, which makes it easier to find roles aligned with your specific status before applying.
Which visa types does Apple commonly use for ML Engineers?
Apple sponsors H-1B for most international ML Engineers and also files for H-1B1 (Chilean and Singaporean nationals), E-3 (Australian nationals), and TN (Canadian and Mexican nationals). Green Card sponsorship through EB-2 or EB-3 PERM is also available for longer-tenured employees. F-1 OPT and CPT are supported for students and recent graduates in qualifying programs.
What qualifications does Apple expect for ML Engineer roles?
Apple's ML Engineer postings consistently require a bachelor's degree or higher in Computer Science, Electrical Engineering, or a related field, which directly supports the H-1B specialty occupation standard. Beyond credentials, Apple looks for hands-on experience with model optimization for constrained hardware, proficiency in frameworks like PyTorch or JAX, and familiarity with low-latency inference requirements common in consumer device contexts.
How do I think about the timeline from offer to start date as an international ML Engineer candidate at Apple?
Timeline depends heavily on your visa category. E-3 and TN candidates can often start within four to six weeks of an offer if the Labor Condition Application is filed promptly with DOL. H-1B candidates without existing status must wait for an approved lottery selection, which runs on a federal fiscal year cycle starting October 1. Premium processing with USCIS is available and reduces H-1B adjudication to 15 business days once filed.
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