TN Visa Machine Learning Intern Jobs
Machine Learning Intern roles qualify for TN visa sponsorship under the Computer Systems Analyst or Engineer scientist categories when the position requires applying ML frameworks to real data problems. Canadian citizens can enter at the border same-day; Mexican citizens apply at a U.S. consulate. Your employer files no lottery paperwork.
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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 TN Visa Machine Learning Intern Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new TN Visa Machine Learning Intern Jobs.
Get Access To All JobsTips for Finding TN Visa Sponsorship as a Machine Learning Intern
Align your transcripts to ML frameworks
TN visa approval hinges on demonstrating a direct link between your degree field and the ML intern role. Pull your unofficial transcript before applications and flag coursework in statistics, neural networks, or data engineering that maps to the job description.
Target employers already filing TN petitions
Search Migrate Mate for employers with recent visa filings who have sponsored Computer Systems Analyst or Engineer roles. Those employers already understand visa sponsorship processes and won't hesitate over whether an ML intern position qualifies for TN status.
Negotiate a support letter before your start date
Canadian citizens need their TN support letter ready at the port of entry, not after onboarding begins. Confirm the employer will issue a detailed offer letter stating your ML duties, degree requirement, and intended duration before you book travel.
Clarify which TN category your role falls under
ML intern postings sometimes blur Computer Systems Analyst and Engineer scientist categories. Ask your hiring manager or recruiter which classification they intend to file under before you reach the offer stage, since the supporting documentation differs between the two.
Use Migrate Mate to filter jobs by visa sponsorship experience
Not every ML internship listing advertises visa support. Use Migrate Mate to surface roles at employers with recent visa filings, so you spend your applications on positions where sponsorship experience is already part of the hiring process.
Prepare for Mexican citizen consular processing timelines
Mexican nationals must schedule a nonimmigrant visa interview at a U.S. consulate before starting work. Build at least four to six weeks into your timeline between offer acceptance and your intended start date to account for appointment availability.
Machine Learning Intern TN Visa: Frequently Asked Questions
Does a Machine Learning Intern role qualify for TN visa sponsorship?
Yes, provided the position genuinely requires a bachelor's degree or higher in a qualifying field such as computer science, engineering, or mathematics. The role must map to an approved TN category, typically Computer Systems Analyst or Engineer scientist. A generic intern title without a clear technical degree requirement can create problems at the port of entry or consulate, so confirm the category with your employer before accepting the offer.
How does TN visa compare to H-1B for Machine Learning Intern positions?
TN has no annual cap and no lottery for Canadian citizens, so you can start as soon as the employer issues a qualifying offer letter and you clear the port of entry. H-1B visa requires lottery selection, a April registration window, and an October 1 start date at the earliest. For internship timelines tied to academic semesters, TN's flexibility makes it far more practical when you're Canadian or Mexican and the role qualifies.
What documents does an employer need to sponsor a TN visa for this role?
The employer must provide a support letter on company letterhead that states the job title, the specific ML duties, the TN category being claimed, your degree and how it relates to the role, and the intended employment period. Canadian citizens present this letter at the U.S. port of entry along with proof of citizenship and their degree credentials. Mexican citizens submit it as part of their DS-160 consular application package.
Where should I search for Machine Learning Intern jobs that offer TN visa sponsorship?
Migrate Mate is built specifically for Canadian and Mexican professionals seeking roles with documented TN visa sponsorship. Most general job boards don't filter by visa category or employer filing history, which means you spend time applying to postings where sponsorship is uncertain. Migrate Mate surfaces ML intern roles at employers who have an established record of supporting TN applicants.
Can a Machine Learning Intern on TN status change employers mid-internship?
You can change employers, but TN status is tied to a specific employer and position. A new TN must be obtained before you begin work with the new company, either through a new port-of-entry entry for Canadians or a consular appointment for Mexican citizens. You cannot simply transfer TN status the way some other visa categories permit, so any employer change requires lead time and coordination with the new hiring team.