Machine Learning Engineer Jobs at Apple with Visa Sponsorship
Machine Learning Engineer jobs at Apple 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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Get Access To All JobsWe're building a massive, real-time search experience that sits at the intersection of Generative AI and Information Retrieval! We make sense of high-volume structured and multimodal data and complex behavioral signals which deliver results that feel instant and relevant while still being private.
Join our team as a ML Search Engineering Manager and take part in this rare opportunity to shape a user-facing product that millions of Apple customers rely on every day!
Description
We are looking for a Search Engineering Manager & Lead to serve as both the senior technical authority and the people leader for our search team. You'll own the architecture and long-term technical roadmap for large-scale, low-latency search infrastructure, from query understanding and hybrid retrieval through ranking and evaluation, and you'll also build, grow, and lead the team of search engineers who bring that roadmap to life.
This is a hands-on leadership role with dual scope: you set the technical vision and personally shape the hardest retrieval and ranking decisions, and you also manage, mentor, and grow the engineers executing against it.
Responsibilities
-
Architecture & Design (Architect scope)
-
Set technical direction: own the architecture and long-term technical roadmap for large-scale, low-latency search infrastructure, making build-vs-buy and platform tradeoffs that the team executes against.
-
Lead query understanding and retrieval strategy: guide the evolution of search pipelines, including autocomplete, query suggestions, and core search, intent classification, entity extraction, semantic parsing, and query expansion, and hybrid retrieval approaches spanning real-time, vector-based, and natural language search.
-
Drive ranking strategy: set direction for relevance and ranking approaches (Learning to Rank, cross-encoder rerankers, multi-stage pipelines), driving AI/ML-powered search quality improvements that deliver measurable relevance gains, and review designs before they ship.
-
Own evaluation rigor: drive the offline evaluation frameworks and online A/B testing methodology the team uses to validate search quality improvements.
-
Track the state of the art: stay current with search and IR research, and translate promising techniques into scalable, production-ready designs for the team to build.
-
Treat privacy as an architectural constraint: apply data minimization and privacy-preserving techniques to any user behavioral signal used in ranking or retrieval.
-
Own safety and trust for generative search results: set the guardrails against hallucination and harmful or misleading AI-generated answers, partnering with Trust & Safety on red-teaming and safety evaluation.
-
Lead the development of generative AI-powered search features, and invest in developer productivity and tooling that let the team ship search capabilities faster.
-
Technical Leadership & Implementation (Lead scope)
-
Raise the technical bar: lead design and code reviews, and establish the engineering standards and best practices the team builds against.
-
Represent the team technically: act as the primary technical voice in cross-functional design reviews with Research Scientists, Product, Data Engineering, MLOps, and Search Infrastructure teams.
-
Unblock the hardest problems: stay hands-on enough to jump into the most ambiguous or highest-risk technical problems, such as scaling bottlenecks, ranking regressions, or novel retrieval techniques, rather than delegating them away.
-
Drive the team's execution against the technical roadmap, from design through production delivery, and communicate progress, trade-offs, and risks to senior leadership and partner orgs.
-
Team Leadership & Management (People scope)
-
Partner with recruiting to attract, evaluate, and hire senior and staff search engineers, raising the technical bar with every hire.
-
Manage a group of search engineers directly, owning their performance, career development, and technical growth, and mentor across levels on search and IR fundamentals, ranking, and retrieval systems.
-
Allocate work against the roadmap, unblock execution, drive design reviews, and hold a high bar for engineering craft and operational excellence.
-
Advocate for the investments the search platform needs, and communicate progress and risk to senior leadership and partner orgs.
-
Cultivate a healthy engineering culture: high ownership, strong review practices, and a deep commitment to search quality and user trust.
Minimum Qualifications
-
MS in Computer Science, Engineering, or a related technical field, or equivalent experience. PhD preferred.
-
12+ years of experience in Machine Learning, Data Science, or Software Engineering, with a significant focus on search infrastructure and information retrieval, including at least 5 years operating in a technical leadership or engineering management capacity.
-
Proven experience leading and managing engineers, including hiring, performance management, and technical mentorship of senior and staff ICs.
-
Track record of leading the architecture of large-scale search systems from design through production.
-
Deep understanding of information retrieval, ranking algorithms, and user modeling techniques.
-
Experience designing offline evaluation frameworks and online A/B testing methodology to validate search relevance and ranking quality.
-
Experience with vector databases (Milvus, Qdrant, Pinecone, or FAISS).
-
Experience with search infrastructure such as OpenSearch, Elasticsearch, or similar stacks.
-
Experience with cloud environments (AWS or GCP), containerization (Docker, Kubernetes), and streaming platforms (Kafka or comparable brokers).
-
Excellent written and verbal communication, with the ability to align engineers, partner teams, and senior leadership around a shared technical direction.
-
Strong proficiency in a systems language such as Go or C++, with working proficiency in Java or Python.
-
Deep familiarity with ML frameworks (TensorFlow, PyTorch, XGBoost, or similar) and ML system design, model lifecycle, and experimentation pipelines.
-
Extensive experience with large datasets, data processing pipelines (Spark, Flink), and scalable architectures.
-
Working knowledge of data privacy principles (e.g., data minimization, privacy-preserving techniques) and experience applying them to systems that use user behavioral signals.
-
Experience implementing safety guardrails for generative AI outputs, including hallucination mitigation, harmful-content filtering, and red-teaming or adversarial evaluation practices.
Preferred Qualifications
-
Published work or patents in search systems, information retrieval, or related ML fields.
-
Strong foundation in deep learning architectures for search and retrieval (transformers, graph neural networks, learned sparse representations).
-
Exposure to multi-objective optimization in search (relevance, diversity, freshness, fairness).
-
Track record of scaling engineering teams and modernizing infrastructure with measurable cost and reliability improvements.
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 $237,600 and $401,700, 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. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
We're building a massive, real-time search experience that sits at the intersection of Generative AI and Information Retrieval! We make sense of high-volume structured and multimodal data and complex behavioral signals which deliver results that feel instant and relevant while still being private.
Join our team as a ML Search Engineering Manager and take part in this rare opportunity to shape a user-facing product that millions of Apple customers rely on every day!
Description
We are looking for a Search Engineering Manager & Lead to serve as both the senior technical authority and the people leader for our search team. You'll own the architecture and long-term technical roadmap for large-scale, low-latency search infrastructure, from query understanding and hybrid retrieval through ranking and evaluation, and you'll also build, grow, and lead the team of search engineers who bring that roadmap to life.
This is a hands-on leadership role with dual scope: you set the technical vision and personally shape the hardest retrieval and ranking decisions, and you also manage, mentor, and grow the engineers executing against it.
Responsibilities
-
Architecture & Design (Architect scope)
-
Set technical direction: own the architecture and long-term technical roadmap for large-scale, low-latency search infrastructure, making build-vs-buy and platform tradeoffs that the team executes against.
-
Lead query understanding and retrieval strategy: guide the evolution of search pipelines, including autocomplete, query suggestions, and core search, intent classification, entity extraction, semantic parsing, and query expansion, and hybrid retrieval approaches spanning real-time, vector-based, and natural language search.
-
Drive ranking strategy: set direction for relevance and ranking approaches (Learning to Rank, cross-encoder rerankers, multi-stage pipelines), driving AI/ML-powered search quality improvements that deliver measurable relevance gains, and review designs before they ship.
-
Own evaluation rigor: drive the offline evaluation frameworks and online A/B testing methodology the team uses to validate search quality improvements.
-
Track the state of the art: stay current with search and IR research, and translate promising techniques into scalable, production-ready designs for the team to build.
-
Treat privacy as an architectural constraint: apply data minimization and privacy-preserving techniques to any user behavioral signal used in ranking or retrieval.
-
Own safety and trust for generative search results: set the guardrails against hallucination and harmful or misleading AI-generated answers, partnering with Trust & Safety on red-teaming and safety evaluation.
-
Lead the development of generative AI-powered search features, and invest in developer productivity and tooling that let the team ship search capabilities faster.
-
Technical Leadership & Implementation (Lead scope)
-
Raise the technical bar: lead design and code reviews, and establish the engineering standards and best practices the team builds against.
-
Represent the team technically: act as the primary technical voice in cross-functional design reviews with Research Scientists, Product, Data Engineering, MLOps, and Search Infrastructure teams.
-
Unblock the hardest problems: stay hands-on enough to jump into the most ambiguous or highest-risk technical problems, such as scaling bottlenecks, ranking regressions, or novel retrieval techniques, rather than delegating them away.
-
Drive the team's execution against the technical roadmap, from design through production delivery, and communicate progress, trade-offs, and risks to senior leadership and partner orgs.
-
Team Leadership & Management (People scope)
-
Partner with recruiting to attract, evaluate, and hire senior and staff search engineers, raising the technical bar with every hire.
-
Manage a group of search engineers directly, owning their performance, career development, and technical growth, and mentor across levels on search and IR fundamentals, ranking, and retrieval systems.
-
Allocate work against the roadmap, unblock execution, drive design reviews, and hold a high bar for engineering craft and operational excellence.
-
Advocate for the investments the search platform needs, and communicate progress and risk to senior leadership and partner orgs.
-
Cultivate a healthy engineering culture: high ownership, strong review practices, and a deep commitment to search quality and user trust.
Minimum Qualifications
-
MS in Computer Science, Engineering, or a related technical field, or equivalent experience. PhD preferred.
-
12+ years of experience in Machine Learning, Data Science, or Software Engineering, with a significant focus on search infrastructure and information retrieval, including at least 5 years operating in a technical leadership or engineering management capacity.
-
Proven experience leading and managing engineers, including hiring, performance management, and technical mentorship of senior and staff ICs.
-
Track record of leading the architecture of large-scale search systems from design through production.
-
Deep understanding of information retrieval, ranking algorithms, and user modeling techniques.
-
Experience designing offline evaluation frameworks and online A/B testing methodology to validate search relevance and ranking quality.
-
Experience with vector databases (Milvus, Qdrant, Pinecone, or FAISS).
-
Experience with search infrastructure such as OpenSearch, Elasticsearch, or similar stacks.
-
Experience with cloud environments (AWS or GCP), containerization (Docker, Kubernetes), and streaming platforms (Kafka or comparable brokers).
-
Excellent written and verbal communication, with the ability to align engineers, partner teams, and senior leadership around a shared technical direction.
-
Strong proficiency in a systems language such as Go or C++, with working proficiency in Java or Python.
-
Deep familiarity with ML frameworks (TensorFlow, PyTorch, XGBoost, or similar) and ML system design, model lifecycle, and experimentation pipelines.
-
Extensive experience with large datasets, data processing pipelines (Spark, Flink), and scalable architectures.
-
Working knowledge of data privacy principles (e.g., data minimization, privacy-preserving techniques) and experience applying them to systems that use user behavioral signals.
-
Experience implementing safety guardrails for generative AI outputs, including hallucination mitigation, harmful-content filtering, and red-teaming or adversarial evaluation practices.
Preferred Qualifications
-
Published work or patents in search systems, information retrieval, or related ML fields.
-
Strong foundation in deep learning architectures for search and retrieval (transformers, graph neural networks, learned sparse representations).
-
Exposure to multi-objective optimization in search (relevance, diversity, freshness, fairness).
-
Track record of scaling engineering teams and modernizing infrastructure with measurable cost and reliability improvements.
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 $237,600 and $401,700, 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. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
See all 285+ Machine Learning Engineer Jobs at Apple
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer Jobs at Apple.
Get Access To All JobsTips for Finding Machine Learning Engineer Jobs at Apple
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.
Distinguish your visa type during the offer stage
Apple sponsors H-1B, E-3, TN, and H-1B1 visa depending on your nationality. If you're Australian or Canadian, flag your eligibility for E-3 or TN during the offer call, since these skip the H-1B lottery and can shorten your start date by months.
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.
Time your application around Apple's hiring cycles
Apple tends to ramp ML hiring in Q1 and Q3. If you're on F-1 OPT, verify your OPT end date against USCIS's 60-day grace period and cap-gap provisions so you know the latest you can accept an offer without a gap in work authorization.
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.
Frequently 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 visa, E-3 visa, TN visa, EB-2, EB-3, F-1 OPT, and F-1 CPT for Machine Learning Engineer roles. E-3 visa is available to Australian citizens, TN visa to Canadian and Mexican nationals, and H-1B1 visa 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.