Machine Learning Manager Jobs at Apple with Visa Sponsorship
Machine Learning Manager jobs at Apple sit at the intersection of research leadership and product scale, overseeing teams that ship ML capabilities across hardware, software, and services. Apple sponsors multiple visa categories for this function, making it a realistic target for international candidates with strong technical leadership backgrounds.
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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 304+ Machine Learning Manager Jobs at Apple
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Manager Jobs at Apple.
Get Access To All JobsTips for Finding Machine Learning Manager Jobs at Apple
Align your portfolio with Apple's ML stack
Apple's ML work spans on-device inference, neural engine optimization, and privacy-preserving models. Frame your experience around deployment constraints and hardware-software co-design, not just model accuracy metrics, so your background maps directly to what hiring managers evaluate.
Target roles requiring cross-functional team leadership
Apple structures ML Manager roles around owning both research direction and engineering execution across product groups. Highlight experience managing mixed teams of researchers and engineers shipping to production, which differentiates you from pure research or pure engineering management candidates.
Prepare your credentials for specialty occupation documentation
For H-1B sponsorship, USCIS requires evidence that the role is a specialty occupation. Gather transcripts, any advanced degree certifications, and documentation of progressively specialized ML work early, before the offer stage, so Apple's immigration counsel can move quickly.
Time your application around H-1B cap and OPT windows
If you're on F-1 OPT, Apple can file an H-1B cap-subject petition in April for an October 1 start. Confirm your OPT expiration date before accepting an offer so your employer's counsel has enough runway to bridge any gap with a cap-gap extension.
Use Migrate Mate to filter verified Apple ML sponsorship openings
Apple posts Machine Learning Manager roles across multiple business units and career sites. Use Migrate Mate to filter active Apple roles that align with your target visa type, so you're applying to positions where sponsorship is already confirmed rather than assumed.
Ask explicitly about PERM timing if you want a Green Card path
For EB-2 or EB-3 sponsorship, Apple's immigration team initiates the DOL PERM labor certification process. Ask during offer negotiation whether Apple starts PERM within the first year of employment, since the earlier you file, the earlier your priority date is established.
Frequently Asked Questions
Does Apple sponsor H-1B visas for Machine Learning Managers?
Yes, Apple sponsors H-1B visas for Machine Learning Manager roles. The H-1B is the most common work visa path for this position given the degree requirements and specialized nature of the work. Apple also sponsors the H-1B1 visa for Singaporean and Chilean nationals, and the E-3 visa for Australian citizens, offering alternatives that bypass the H-1B lottery entirely.
How do I apply for Machine Learning Manager jobs at Apple?
Apply directly through Apple's careers site or use Migrate Mate to browse verified Machine Learning Manager openings at Apple filtered by your visa type. Apple's hiring process for this level typically includes a recruiter screen, technical assessments on ML system design and leadership scenarios, and several rounds of cross-functional interviews before an offer is extended.
Which visa types does Apple commonly use for Machine Learning Manager roles?
Apple sponsors H-1B, H-1B1 visa, E-3, and TN visas for nonimmigrant work authorization in this role. For permanent residency, Apple files EB-2 and EB-3 petitions through the DOL PERM labor certification process. F-1 OPT and CPT are also supported, giving candidates already in the U.S. on student status a bridge while H-1B sponsorship is initiated.
What qualifications does Apple expect for a Machine Learning Manager?
Apple typically expects a graduate degree in machine learning, computer science, or a related field, combined with demonstrated experience managing ML teams that have shipped products at scale. Hands-on technical depth is weighted heavily alongside leadership credentials. Experience with on-device ML, privacy-preserving techniques, or Apple's Neural Engine is a distinguishing factor at the manager level.
How do I handle the timing between an Apple offer and my visa filing?
Once Apple extends an offer, their immigration counsel files the Labor Condition Application with the DOL before submitting the H-1B petition to USCIS. The full process from offer to approved status can take three to six months without premium processing. If you're transferring from another employer, Apple can file an H-1B transfer petition and you can start on a receipt notice under portability rules.