Senior ML Engineer Jobs at Apple with Visa Sponsorship
Senior ML Engineer jobs at Apple involve building some of the company's most ambitious machine learning infrastructure across on-device intelligence, Siri, computer vision, and silicon optimization. Apple has a strong track record of sponsoring work visas across multiple categories for qualified ML engineers, making it a viable path 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 285+ Senior ML Engineer Jobs at Apple
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Senior ML Engineer Jobs at Apple.
Get Access To All JobsTips for Finding Senior ML Engineer Jobs at Apple
Align your portfolio to Apple's ML domains
Apple's ML hiring concentrates on on-device inference, CoreML, neural engine optimization, and privacy-preserving techniques. Frame past projects around model efficiency and deployment constraints, not just research accuracy, before you apply.
Target teams beyond Siri and Photos
Apple's Health, Maps, and silicon teams actively hire Senior ML Engineers and are less saturated with applicants. Roles tied to Apple Silicon's Neural Engine often require deeper systems knowledge, which narrows the field in your favor.
Confirm your visa category before accepting an offer
Apple sponsors H-1B, E-3, TN, and F-1 OPT among others, but the right category depends on your nationality and status. Clarify which petition Apple's immigration team will file before you negotiate, since each category carries different timelines.
Start the H-1B lottery registration early in your job search
If you need H-1B sponsorship, USCIS registration opens in March for an October 1 start date. Targeting Apple roles in Q4 of the prior year gives your recruiter enough runway to register you in that cycle.
Request premium processing if your OPT window is tight
F-1 OPT gives you a 60-day grace period after employment ends. If Apple's offer timeline is running close to your OPT expiration, ask the recruiting team whether USCIS premium processing is available for your petition category.
Use Migrate Mate to filter Apple's open Senior ML Engineer roles by visa type
Sponsorship eligibility varies by role and team at Apple. Use Migrate Mate to surface Senior ML Engineer postings at Apple filtered by the visa categories you qualify for, so you apply where sponsorship is already confirmed.
Frequently Asked Questions
Does Apple sponsor H-1B visas for Senior ML Engineers?
Yes, Apple sponsors H-1B visas for Senior ML Engineers. Apple participates in the annual USCIS H-1B lottery registration each March and files cap-subject petitions for selected candidates. If you're already in H-1B visa status with another employer, Apple can also file an H-1B transfer, which lets you start work once the petition is received by USCIS, without waiting for a full approval.
How do I apply for Senior ML Engineer jobs at Apple?
Applications go through Apple's careers portal at jobs.apple.com. Senior ML Engineer roles at Apple typically require you to pass a recruiter screen, a technical phone interview focused on ML fundamentals and system design, and a full loop of four to six interviews covering modeling, coding, and cross-functional collaboration. You can also browse open roles filtered by visa sponsorship eligibility on Migrate Mate before applying directly.
Which visa types does Apple commonly sponsor for Senior ML Engineers?
Apple sponsors several visa categories for Senior ML Engineers depending on your nationality and current status. H-1B and Green Card sponsorship through EB-2 or EB-3 PERM are available for most nationalities. Australian citizens can pursue the E-3 visa, which has no lottery. Canadian and Mexican nationals may qualify for TN visa status. F-1 OPT and CPT are also supported, covering recent graduates in active OPT periods.
What qualifications does Apple expect for Senior ML Engineer roles?
Apple's Senior ML Engineer postings consistently require a graduate degree in machine learning, computer science, or a related field, alongside hands-on experience deploying models in production environments. Proficiency in Python and a deep learning framework like PyTorch or JAX is expected. Roles tied to Apple Silicon or on-device inference also expect familiarity with model quantization, pruning, and hardware-aware optimization, which goes beyond typical research-focused ML backgrounds.
How do I think about timing if I need Apple to sponsor my visa?
Timing depends on your current status. H-1B cap-subject petitions have a fixed annual cycle, so an offer in late spring may mean waiting until October 1 for your start date. E-3 visa and TN sponsorship can move faster since neither requires lottery selection. PERM-based Green Card sponsorship runs in parallel with employment and takes significantly longer. Discuss your status with Apple's immigration team during the offer stage, not after signing.