AI ML Intern Jobs in USA with Visa Sponsorship
AI/ML intern roles are among the most actively sponsored positions in tech, with employers regularly filing H-1B visa and OPT-eligible roles for candidates in computer science, data science, and related fields. Most require enrollment in a qualifying degree program and authorization to work through CPT or OPT. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsDepartment Description:
At Disney, we’re storytellers. We make the impossible, possible. The Walt Disney Company (TWDC) is a world-class entertainment and technological leader. Walt’s passion was to continuously envision new ways to move audiences around the world—a passion that remains our touchstone in an enterprise that stretches from theme parks, resorts and a cruise line to sports, news, movies and a variety of other businesses. Uniting each endeavor is a commitment to creating and delivering unforgettable experiences — and we’re constantly looking for new ways to enhance these exciting experiences.
The Enterprise Technology mission is to deliver technology solutions that align to business strategies while enabling enterprise efficiency and promoting cross-company collaborative innovation. Our group drives competitive advantage by enhancing our consumer experiences, enabling business growth, and advancing operational excellence.
Team Description:
We are the Finance Engineering & AI team, and we exist to be Finance's technical partner — for FP&A, Payroll, Controllership, and Tax & Treasury alike — turning business process needs into working software and intelligent automation. Our AI Platform team designs and builds the intelligent automation layer that streamlines and scales finance workflows, while our Business Process Applications team owns and operates the core systems finance relies on every day. Together, we combine deep finance domain expertise with custom engineering to give Finance both a stable operational backbone and a path toward an increasingly automated future.
What You’ll Do:
Disney Financial Insights (D-Fi) is a new platform reimagining how Finance teams and budget owners across Disney interact with financial data — replacing manual pulls, static reports, and email-driven approvals with one AI-powered, orchestrated experience built on top of Disney financial systems.
We're hiring a Senior Manager, AI & Machine Learning, as the founding engineering leader for this AI framework: a hands-on manager and technical lead who will build and run a global engineering team based in Buenos Aires AR, delivering the AI/ML layer, the intelligent data foundation, software workflows/orchestration, and natural-language experience that sits at the center of D-Fi.
This is a build-from-scratch mandate — you'll be shaping team structure, engineering practices, and technical architecture from an early stage (MVP in progress), not inheriting a mature system.
This role is the primary technical point of contact for finance stakeholders & cross-functional partners and is accountable for translating the platform roadmap into a shipped, governed, production-grade system.
As the Senior Manager, AI & Machine Learning, you'll be setting architecture, reviewing design decisions, and staying hands-on enough to credibly evaluate trade-offs your team brings you. This role is also expected to be hands-on — stepping in on implementation, unblocking technical issues in real time, and reviewing code with the depth that comes from doing the work yourself, not just overseeing it.
-
Own technical direction end-to-end. Set architecture and technical strategy for the AI/ML and data layers underpinning D-Fi, ensuring every capability — variance analysis, scenario modeling, the natural-language experience — is built on a governed, semantic data foundation rather than one-off pipelines.
-
Be the stakeholder-facing technical lead. Act as the primary engineering liaison to finance business partners and leadership — translating challenging business needs into a scoped, buildable technical roadmap, and representing engineering trade-offs credibly to technical and non-technical partners and leaders.
-
Own the AI/LLM roadmap. This is the highest-visibility and most sensitive capability on the platform — you'll guide its direction end-to-end to build and maintain trust in AI-generated financial answers.
-
Enforce governance by design. Ensure role-based access control (RBAC) is enforced at the data model level — not the application level — across all personas (Finance, Budget Owners, and any future personas), so speed and automation never come at the cost of data governance.
-
Set the bar for engineering quality and delivery. Establish practices for code review, testing, model evaluation, and release management across a geo diverse team and multiple disciplines.
-
Own the build vs. integrate decisions. Guide when the team builds custom capability versus integrates with existing systems (SAP, Cognos, EPM, Data Marketplace, NetDocs, Coupa, Clarity).
-
Champion adoption broadly. Partner with the business finance transformation leads on driving adoption across the enterprise — both finance workers and budget owners.
Required Qualifications & Skills:
-
10+ years of software and/or ML engineering leadership experience, with a strong track record of owning technical direction and architecture for complex systems end-to-end.
-
Demonstrated experience architecting and shipping production LLM/AI applications — not just prototypes.
-
Production experience with LLM application architecture — RAG pipelines, prompt engineering, and context/retrieval design at scale.
-
Working knowledge of LLM orchestration frameworks (e.g., LangChain, LlamaIndex, or equivalent) and vector databases/retrieval stores (e.g., Pinecone, Weaviate, pgvector, or Snowflake Cortex Search).
-
Strong data modeling background, ideally including semantic/governed data layers (not just raw pipelines).
-
Ability to read, write and review Python code.
-
Working knowledge of modern web/full-stack language or framework sufficient to guide UI-layer architecture decisions.
-
Familiarity with MLOps practices — model monitoring, retraining pipeline design, containerization, and CI/CD for ML systems.
-
Experience working across distributed, cross-timezone engineering teams, with the judgment to know what belongs in synchronous vs. asynchronous workflows.
-
Comfort operating as the primary technical voice in front of senior business stakeholders — able to translate finance/business problems into technical scope, and technical trade-offs into business language.
Preferred Qualifications:
-
Background building internal tools for finance organizations is a plus, but not required — genuine curiosity about the domain is.
-
Exposure to ERP/EPM platforms such as SAP, Oracle EPM, or Cognos, and/or experience integrating custom applications against enterprise system APIs.
-
Experience working alongside or evaluating third-party/consulting-delivered engineering work, with the judgment to assess technical quality and architectural fit quickly.
Required Education:
- Bachelor's degree in Computer Science, Engineering, or related field; advanced degree a plus but not required given equivalent experience.
LOCATION:
Location: Buenos Aires AR
The hiring range for this position in Orlando, FL is $197,600-$264,900 and in Burbank, CA is $207,400-$278,100 and in Seattle, WA is $217,300-$291,500. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
Department Description:
At Disney, we’re storytellers. We make the impossible, possible. The Walt Disney Company (TWDC) is a world-class entertainment and technological leader. Walt’s passion was to continuously envision new ways to move audiences around the world—a passion that remains our touchstone in an enterprise that stretches from theme parks, resorts and a cruise line to sports, news, movies and a variety of other businesses. Uniting each endeavor is a commitment to creating and delivering unforgettable experiences — and we’re constantly looking for new ways to enhance these exciting experiences.
The Enterprise Technology mission is to deliver technology solutions that align to business strategies while enabling enterprise efficiency and promoting cross-company collaborative innovation. Our group drives competitive advantage by enhancing our consumer experiences, enabling business growth, and advancing operational excellence.
Team Description:
We are the Finance Engineering & AI team, and we exist to be Finance's technical partner — for FP&A, Payroll, Controllership, and Tax & Treasury alike — turning business process needs into working software and intelligent automation. Our AI Platform team designs and builds the intelligent automation layer that streamlines and scales finance workflows, while our Business Process Applications team owns and operates the core systems finance relies on every day. Together, we combine deep finance domain expertise with custom engineering to give Finance both a stable operational backbone and a path toward an increasingly automated future.
What You’ll Do:
Disney Financial Insights (D-Fi) is a new platform reimagining how Finance teams and budget owners across Disney interact with financial data — replacing manual pulls, static reports, and email-driven approvals with one AI-powered, orchestrated experience built on top of Disney financial systems.
We're hiring a Senior Manager, AI & Machine Learning, as the founding engineering leader for this AI framework: a hands-on manager and technical lead who will build and run a global engineering team based in Buenos Aires AR, delivering the AI/ML layer, the intelligent data foundation, software workflows/orchestration, and natural-language experience that sits at the center of D-Fi.
This is a build-from-scratch mandate — you'll be shaping team structure, engineering practices, and technical architecture from an early stage (MVP in progress), not inheriting a mature system.
This role is the primary technical point of contact for finance stakeholders & cross-functional partners and is accountable for translating the platform roadmap into a shipped, governed, production-grade system.
As the Senior Manager, AI & Machine Learning, you'll be setting architecture, reviewing design decisions, and staying hands-on enough to credibly evaluate trade-offs your team brings you. This role is also expected to be hands-on — stepping in on implementation, unblocking technical issues in real time, and reviewing code with the depth that comes from doing the work yourself, not just overseeing it.
-
Own technical direction end-to-end. Set architecture and technical strategy for the AI/ML and data layers underpinning D-Fi, ensuring every capability — variance analysis, scenario modeling, the natural-language experience — is built on a governed, semantic data foundation rather than one-off pipelines.
-
Be the stakeholder-facing technical lead. Act as the primary engineering liaison to finance business partners and leadership — translating challenging business needs into a scoped, buildable technical roadmap, and representing engineering trade-offs credibly to technical and non-technical partners and leaders.
-
Own the AI/LLM roadmap. This is the highest-visibility and most sensitive capability on the platform — you'll guide its direction end-to-end to build and maintain trust in AI-generated financial answers.
-
Enforce governance by design. Ensure role-based access control (RBAC) is enforced at the data model level — not the application level — across all personas (Finance, Budget Owners, and any future personas), so speed and automation never come at the cost of data governance.
-
Set the bar for engineering quality and delivery. Establish practices for code review, testing, model evaluation, and release management across a geo diverse team and multiple disciplines.
-
Own the build vs. integrate decisions. Guide when the team builds custom capability versus integrates with existing systems (SAP, Cognos, EPM, Data Marketplace, NetDocs, Coupa, Clarity).
-
Champion adoption broadly. Partner with the business finance transformation leads on driving adoption across the enterprise — both finance workers and budget owners.
Required Qualifications & Skills:
-
10+ years of software and/or ML engineering leadership experience, with a strong track record of owning technical direction and architecture for complex systems end-to-end.
-
Demonstrated experience architecting and shipping production LLM/AI applications — not just prototypes.
-
Production experience with LLM application architecture — RAG pipelines, prompt engineering, and context/retrieval design at scale.
-
Working knowledge of LLM orchestration frameworks (e.g., LangChain, LlamaIndex, or equivalent) and vector databases/retrieval stores (e.g., Pinecone, Weaviate, pgvector, or Snowflake Cortex Search).
-
Strong data modeling background, ideally including semantic/governed data layers (not just raw pipelines).
-
Ability to read, write and review Python code.
-
Working knowledge of modern web/full-stack language or framework sufficient to guide UI-layer architecture decisions.
-
Familiarity with MLOps practices — model monitoring, retraining pipeline design, containerization, and CI/CD for ML systems.
-
Experience working across distributed, cross-timezone engineering teams, with the judgment to know what belongs in synchronous vs. asynchronous workflows.
-
Comfort operating as the primary technical voice in front of senior business stakeholders — able to translate finance/business problems into technical scope, and technical trade-offs into business language.
Preferred Qualifications:
-
Background building internal tools for finance organizations is a plus, but not required — genuine curiosity about the domain is.
-
Exposure to ERP/EPM platforms such as SAP, Oracle EPM, or Cognos, and/or experience integrating custom applications against enterprise system APIs.
-
Experience working alongside or evaluating third-party/consulting-delivered engineering work, with the judgment to assess technical quality and architectural fit quickly.
Required Education:
- Bachelor's degree in Computer Science, Engineering, or related field; advanced degree a plus but not required given equivalent experience.
LOCATION:
Location: Buenos Aires AR
The hiring range for this position in Orlando, FL is $197,600-$264,900 and in Burbank, CA is $207,400-$278,100 and in Seattle, WA is $217,300-$291,500. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
See all 18,663+ AI ML Intern Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new AI ML Intern roles.
Get Access To All JobsTips for Finding Visa Sponsorship as an AI ML Intern
Confirm CPT or OPT eligibility before applying
Most AI/ML internships are filled through Curricular Practical Training or Optional Practical Training, not H-1B. Verify your F-1 status allows CPT for the term you plan to work before submitting any application.
Target employers with a history of OPT-to-H-1B conversion
Some employers sponsor interns through OPT and then file H-1B petitions for strong performers. Research companies with consistent H-1B filing histories in machine learning and software engineering roles before accepting an offer.
Align your degree field with the internship description
Specialty occupation requirements mean your degree field must directly relate to the role. A computer science or statistics degree supports most AI/ML positions, while an unrelated major can create sponsorship complications later.
Ask about return offer and full-time sponsorship policies early
Ask recruiters directly whether the company sponsors H-1B petitions for international interns who receive full-time offers. Getting clarity before accepting saves time if the employer only hires citizens and permanent residents for permanent roles.
Prioritize cap-exempt employers for maximum flexibility
Universities, nonprofit research institutions, and government-affiliated labs are exempt from the H-1B cap. AI/ML internships at these organizations offer a path to sponsored employment without depending on the annual lottery.
Have your I-20 and CPT authorization ready before your start date
Employers cannot let you begin work without valid CPT authorization on your I-20. Coordinate with your Designated School Official weeks in advance to avoid delays that could push back your internship start date.
Frequently Asked Questions
Can international students work as AI/ML interns in the U.S. on an F-1 visa?
Yes, F-1 students can work as AI/ML interns using Curricular Practical Training during their program or Optional Practical Training after graduation. CPT requires your internship to be directly tied to your major and approved by your school. OPT gives you up to 12 months of work authorization, with a 24-month STEM extension available for qualifying degrees in computer science, data science, or a related field.
Do AI/ML internships count toward STEM OPT extension eligibility?
They can, provided your degree is on the STEM Designated Degree Program List and your employer is enrolled in E-Verify. Computer science, electrical engineering, statistics, and mathematics degrees typically qualify. The internship itself does not determine STEM OPT eligibility, but working in a role directly related to your STEM degree is required to maintain valid status during the extension period.
Which employers are most likely to sponsor AI/ML interns for H-1B after a full-time offer?
Large technology companies, AI-focused startups with institutional funding, and research-heavy firms in finance and healthcare have the strongest track records of converting international interns to H-1B visa employees. Cap-exempt employers including universities and nonprofit research labs can file H-1B petitions outside the lottery entirely. You can browse AI/ML roles from verified sponsoring employers on Migrate Mate to identify companies with active sponsorship histories.
Does my degree need to be in computer science to qualify for an AI/ML intern role under specialty occupation rules?
Not strictly, but your degree must be directly relevant to the specific role. Computer science, data science, mathematics, statistics, and electrical engineering degrees are the most defensible. A degree in a loosely related field, such as economics with a data focus, may qualify if the job description emphasizes quantitative skills. Roles requiring only general analytical ability without a specific degree field are harder to support under specialty occupation standards.
What happens to my work authorization if my AI/ML internship ends before my CPT end date?
If your internship ends early, you must stop working immediately. CPT authorization is tied to a specific employer and time period, so you cannot transfer it to another company or extend it without a new I-20 from your school. Notify your Designated School Official promptly. Unauthorized work, even briefly, can jeopardize your F-1 status and future visa applications, including any pending OPT or H-1B filings.
What is the prevailing wage requirement for sponsored AI ML Intern jobs?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.