Machine Learning Scientist Jobs at Apple with Visa Sponsorship
Machine Learning Scientist jobs at Apple involve building some of the company's most consequential research teams, with roles spanning on-device intelligence, natural language processing, and computer vision. Apple has a well-established sponsorship track record for this function and supports multiple visa pathways for international candidates.
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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 281+ Machine Learning Scientist Jobs at Apple
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Scientist Jobs at Apple.
Get Access To All JobsTips for Finding Machine Learning Scientist Jobs at Apple
Align your research portfolio to Apple's ML focus areas
Apple prioritizes on-device ML, privacy-preserving machine learning, and neural engine optimization. Tailoring your published work, GitHub projects, or patents to these areas signals fit before your resume reaches a recruiter.
Target teams through Apple's research publication record
Apple ML researchers publish under groups like Siri, Core ML, and Vision. Identifying specific teams via their public papers helps you name the right organization in your application and network toward the right hiring manager.
Verify your visa type early in the interview process
Apple sponsors H-1B, E-3, TN, and F-1 OPT among other categories. If you're nearing the end of OPT, confirm with the recruiter whether Apple's legal team can file before your authorization expires, since processing timelines matter.
Prepare a degree equivalency letter if your credential is non-U.S.
For H-1B specialty occupation petitions, USCIS scrutinizes foreign degree equivalency. Having a credential evaluation from a NACES-member organization ready before offer stage avoids delays once Apple's immigration counsel begins the I-129 filing.
Use Migrate Mate to filter open Machine Learning Scientist roles by visa type
Sponsorship eligibility varies by role and team even within Apple. Use Migrate Mate to surface active postings filtered to your specific visa category so you're applying to positions Apple is actively sponsoring for that function.
Request a start date that accommodates DOL and USCIS timelines
Once Apple's legal team initiates the H-1B process, the Labor Condition Application must clear DOL before the I-129 is filed with USCIS. Negotiating a start date at least 90 days out reduces the risk of a gap in work authorization.
Frequently Asked Questions
Does Apple sponsor H-1B visas for Machine Learning Scientists?
Yes, Apple sponsors H-1B visas for Machine Learning Scientists. The company works with in-house immigration counsel to manage LCA filings with the DOL and I-129 petitions with USCIS. Because Machine Learning Scientist roles qualify as specialty occupations requiring at minimum a bachelor's degree in a directly related field like computer science or statistics, they're well-suited for H-1B sponsorship.
How do I apply for Machine Learning Scientist jobs at Apple?
Applications go through Apple's careers portal at jobs.apple.com. Roles are listed under Research and Machine Learning or within specific product organizations like Siri, Core ML, or Health. You can also find and filter open Machine Learning Scientist positions at Apple by visa type on Migrate Mate, which surfaces active sponsorship-eligible postings in one place.
Which visa types does Apple commonly sponsor for Machine Learning Scientist roles?
Apple sponsors H-1B and H-1B1 visas for most international candidates in this function. Australian citizens are eligible for the E-3 visa, which has no lottery and is processed separately from the H-1B cap. Canadian and Mexican nationals may qualify under TN visa status. F-1 OPT and CPT are also supported for students in qualifying programs, and Apple files EB-2 and EB-3 petitions for permanent residence.
What qualifications does Apple expect for Machine Learning Scientist roles?
Most Machine Learning Scientist postings at Apple require a PhD or a master's degree with substantial research experience in machine learning, computer science, or a closely related quantitative field. Apple's teams value publication records, experience with large-scale model training, and proficiency in frameworks like PyTorch or JAX. Roles focused on on-device ML also expect familiarity with model compression, quantization, or neural architecture search.
How do I estimate the timeline from offer to work authorization at Apple?
After accepting an offer, Apple's immigration team typically initiates the LCA filing with DOL, which takes around seven business days under standard processing. The I-129 H-1B petition then goes to USCIS. Standard processing takes several months, but premium processing reduces USCIS adjudication to 15 business days. For E-3 visa applicants, consular processing in Australia can move faster. Plan on at least 60 to 90 days from offer to start date for most pathways.