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 JobsJoin the team redefining what a deeply personal and integrated assistant can be.
As part of the Siri organization, you will help shape one of the world's most widely used AI assistants, powered by our next-generation of Apple Intelligence, with capabilities like personal context understanding and on-screen awareness, built with privacy from the ground up. Your work will have direct, meaningful impact for users across iOS, iPadOS, macOS, watchOS, and visionOS.
This is a rare opportunity to build at the intersection of cutting-edge AI and human-centered design, shipping technology that is centered around users and their needs.
Description
We are the team building products for voice, dictation and other audio products at Apple. These are multimodal models that power Siri on-device speech features, and the next generation of audio experiences across our platforms. Our researchers and modeling engineers train models, iterate on data mixtures spanning conductor backed Siri telemetry to synthetic voice corpora, and stack supervised fine-tuning, LoRA adapter training, and reinforcement learning into pipelines that produce the adapters, tokenizers and detokenizers.
You’ll join a small group of production automation engineers whose mandate is to turn the operational substrate underneath foundation model training into a reliable, observable, self-serve system. The work spans python, shell tooling, cloud platform integration, internal CLI design, and close partnership with the product and research teams you are enabling.
Responsibilities
-
Own the end-to-end model lifecycle building model pipelines, integrating with other Apple frameworks to enable rapid model iteration, staging promotion, production rollout and deprecation.
-
Design and operate agent-based automation pipelines for ML models where agents own decision logic at each gate and humans approve only at defined escalation points.
-
Develop multi-agent workflows using LLM-native tooling for on-device evaluation, regression triage, release readiness decisions, and automated root cause analysis.
-
Own the launch tooling to build and improve the shell scripts and CLI commands that turn a config-name and a dataset into a running training job - across SFT, LoRA adapter, and RL phases.
Minimum Qualifications
-
Strong software engineering fundamentals; comfortable in Python and Bash, comfortable reading and refactoring large internal codebases.
-
5+ years experience in Machine Learning Operations.
-
Production experience with one or more cloud ML platforms (GCP TPU, AWS GPU clusters, Kubernetes-backed training infra) including submitting jobs, debugging schedulers, working around quota systems.
-
Familiarity with the ML training lifecycle: data preprocessing pipelines, distributed training, checkpoint formats, multi-slice / multi-region considerations.
-
Experience with infrastructure-as-code, CLI tool design, and developer ergonomics. You've shipped tools that other engineers actually use.
-
Bias toward observability and reliability.
-
Comfortable working across team boundaries: you'll partner with researchers, product and infra teams.
Preferred Qualifications
-
Bachelors degree in Computer Science or equivalent technical discipline.
-
Hands-on with JAX, XLA, or large-model training stacks or equivalent.
-
Experience with multi-slice TPU training and cross-region GCS / S3-compatible storage.
-
Background in MLOps tools: model registries, feature stores, experiment trackers, reward-model serving for RL.
-
Prior work simplifying onboarding and access provisioning (Apple Access Manager, AWS IAM at scale, or equivalent).
-
Experience writing Claude Code / agent skills, runbooks, or other LLM-assisted developer tooling.
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 $181,100 and $318,400, 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.
Join the team redefining what a deeply personal and integrated assistant can be.
As part of the Siri organization, you will help shape one of the world's most widely used AI assistants, powered by our next-generation of Apple Intelligence, with capabilities like personal context understanding and on-screen awareness, built with privacy from the ground up. Your work will have direct, meaningful impact for users across iOS, iPadOS, macOS, watchOS, and visionOS.
This is a rare opportunity to build at the intersection of cutting-edge AI and human-centered design, shipping technology that is centered around users and their needs.
Description
We are the team building products for voice, dictation and other audio products at Apple. These are multimodal models that power Siri on-device speech features, and the next generation of audio experiences across our platforms. Our researchers and modeling engineers train models, iterate on data mixtures spanning conductor backed Siri telemetry to synthetic voice corpora, and stack supervised fine-tuning, LoRA adapter training, and reinforcement learning into pipelines that produce the adapters, tokenizers and detokenizers.
You’ll join a small group of production automation engineers whose mandate is to turn the operational substrate underneath foundation model training into a reliable, observable, self-serve system. The work spans python, shell tooling, cloud platform integration, internal CLI design, and close partnership with the product and research teams you are enabling.
Responsibilities
-
Own the end-to-end model lifecycle building model pipelines, integrating with other Apple frameworks to enable rapid model iteration, staging promotion, production rollout and deprecation.
-
Design and operate agent-based automation pipelines for ML models where agents own decision logic at each gate and humans approve only at defined escalation points.
-
Develop multi-agent workflows using LLM-native tooling for on-device evaluation, regression triage, release readiness decisions, and automated root cause analysis.
-
Own the launch tooling to build and improve the shell scripts and CLI commands that turn a config-name and a dataset into a running training job - across SFT, LoRA adapter, and RL phases.
Minimum Qualifications
-
Strong software engineering fundamentals; comfortable in Python and Bash, comfortable reading and refactoring large internal codebases.
-
5+ years experience in Machine Learning Operations.
-
Production experience with one or more cloud ML platforms (GCP TPU, AWS GPU clusters, Kubernetes-backed training infra) including submitting jobs, debugging schedulers, working around quota systems.
-
Familiarity with the ML training lifecycle: data preprocessing pipelines, distributed training, checkpoint formats, multi-slice / multi-region considerations.
-
Experience with infrastructure-as-code, CLI tool design, and developer ergonomics. You've shipped tools that other engineers actually use.
-
Bias toward observability and reliability.
-
Comfortable working across team boundaries: you'll partner with researchers, product and infra teams.
Preferred Qualifications
-
Bachelors degree in Computer Science or equivalent technical discipline.
-
Hands-on with JAX, XLA, or large-model training stacks or equivalent.
-
Experience with multi-slice TPU training and cross-region GCS / S3-compatible storage.
-
Background in MLOps tools: model registries, feature stores, experiment trackers, reward-model serving for RL.
-
Prior work simplifying onboarding and access provisioning (Apple Access Manager, AWS IAM at scale, or equivalent).
-
Experience writing Claude Code / agent skills, runbooks, or other LLM-assisted developer tooling.
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 $181,100 and $318,400, 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.