ML Research Engineer Jobs at Apple with Visa Sponsorship
ML Research Engineer jobs at Apple sit at the intersection of fundamental research and product-scale deployment, covering areas like neural architecture, on-device inference, and silicon-aware model optimization. Apple has a consistent track record of sponsoring work visas for this function across multiple visa categories.
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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 295+ ML Research Engineer Jobs at Apple
Sign up for free to unlock all listings, filter by visa type, and get alerts for new ML Research Engineer Jobs at Apple.
Get Access To All JobsTips for Finding ML Research Engineer Jobs at Apple
Align your research to Apple Silicon
Apple's ML hiring strongly favors candidates with experience optimizing models for constrained hardware environments. Frame your resume and publications around on-device inference, quantization, or neural engine efficiency to match what Apple's teams are actively building.
Target teams through published research
Apple's ML Research Engineers often publish through CVPR, NeurIPS, and ICML. Identify the specific Apple research group whose papers align with your work, then apply to roles tied to that team rather than casting broadly across all open positions.
Clarify your visa category before interviewing
Apple sponsors several visa types for this role. Know which category applies to your situation before your recruiter screen so you can ask targeted questions about their process for that specific visa, not just general sponsorship willingness.
Prepare for the H-1B cap timeline early
If your offer lands outside a cap-exempt pathway, H-1B registration opens in March with an October 1 start date. Coordinating your offer acceptance, I-129 filing, and start date around this window requires planning months before you'd actually begin work.
Document specialty occupation evidence thoroughly
USCIS scrutinizes ML roles when the job description uses broad language. Work with Apple's immigration counsel to ensure the LCA and I-129 petition specifically describe the theoretical and applied research duties that require an advanced degree in a relevant field.
Search verified sponsoring roles on Migrate Mate
Confirming which ML Research Engineer openings at Apple are actively tied to sponsorship saves time during a targeted job search. Migrate Mate filters roles by visa type so you reach out to the right positions from the start.
Frequently Asked Questions
Does Apple sponsor H-1B visas for ML Research Engineers?
Yes, Apple sponsors H-1B visas for ML Research Engineer roles. Apple files petitions through the standard USCIS cap process as well as through cap-exempt pathways where applicable. Because ML Research is a specialized function requiring advanced technical credentials, Apple's immigration team treats these roles as clear specialty occupation cases when preparing the petition documentation.
How do I apply for ML Research Engineer jobs at Apple?
Apply directly through Apple's careers portal, but increase your chances by targeting the specific research domain you work in rather than applying to every open role. Apple's ML Research teams are organized around areas like vision, speech, and on-device learning. Tailoring your application materials to the relevant team's published work signals genuine fit. You can browse currently open roles with confirmed sponsorship through Migrate Mate.
Which visa types does Apple commonly use for ML Research Engineers?
Apple sponsors H-1B, H-1B1 visa, E-3 visa, TN visa, and F-1 OPT and CPT for ML Research Engineer roles, as well as immigrant pathways including EB-2 and EB-3. The right category depends on your citizenship. Australian citizens typically pursue the E-3 visa, Canadians and Mexicans use TN visa, and nationals from most other countries enter through H-1B, subject to the annual lottery.
What qualifications does Apple expect for ML Research Engineer roles?
Apple's ML Research Engineer positions typically require a PhD or a master's degree with substantial research output in machine learning, computer vision, natural language processing, or a closely related discipline. Publications at venues like NeurIPS, ICML, CVPR, or ICLR carry significant weight. Applied experience with large-scale model training, framework-level optimization, or hardware-aware ML is often expected alongside academic credentials.
How do I think about the timeline from offer to work authorization at Apple?
Timeline depends on your visa category. E-3 and TN holders can often start within four to eight weeks of an offer if consular appointments are available. H-1B cap cases require your start date to align with the October 1 fiscal year, meaning an offer in spring may involve a six-month gap before your first day. F-1 OPT holders can start sooner if their OPT authorization is already active.