AI ML Engineering Jobs in USA with Visa Sponsorship
AI and ML engineering roles are among the most actively sponsored positions in the U.S. tech industry, with H-1B visa approval rates well above average for software-adjacent specialties. Employers filing for these roles typically require a master's or PhD in computer science, statistics, or a related quantitative field. For detailed occupation requirements, see the O*NET profile.
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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 18,406+ AI ML Engineering Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new AI ML Engineering roles.
Get Access To All JobsTips for Finding AI ML Engineering Jobs
Target companies with a track record of ML hiring
Companies that have filed LCAs for machine learning engineer and research scientist roles consistently are far more likely to sponsor again. Established ML teams at larger firms have internal immigration infrastructure that makes the process faster and less uncertain for you.
Lead with your ML stack, not just your job title
Recruiters screening for sponsorship-eligible candidates want to see specific frameworks. PyTorch, TensorFlow, JAX, and Hugging Face experience signal genuine ML depth and make it easier for hiring managers to justify the sponsorship investment with supporting documentation.
A master's or PhD removes a major sponsorship barrier
Specialty occupation approval for AI and ML roles is significantly stronger when your degree directly matches the role. Computer science, electrical engineering, statistics, or applied mathematics degrees make the H-1B petition far cleaner for your employer's immigration attorney.
Research roles at universities and labs often offer cap-exempt H-1B sponsorship
National labs, research universities, and affiliated nonprofits can file H-1B petitions year-round without entering the lottery. If you're open to research-oriented ML positions, these employers give you a path to status that bypasses the annual cap entirely.
Quantify your model impact in every application
Sponsoring employers need to demonstrate the role requires a highly specialized professional. Framing your experience around measurable outcomes, such as accuracy improvements, latency reductions, or production-scale deployment, strengthens both your application and the eventual visa petition.
Browse visa-verified AI and ML roles on Migrate Mate
Not every job posting that mentions ML is open to sponsorship candidates. Migrate Mate filters specifically for employers willing to sponsor, so you're not wasting applications on roles that will screen you out the moment sponsorship comes up in conversation.
Frequently Asked Questions
Do AI and ML engineering roles qualify for H-1B specialty occupation status?
Yes, and approval rates for ML engineering roles are consistently strong. USCIS treats positions requiring a bachelor's degree or higher in computer science, statistics, mathematics, or a closely related field as specialty occupations. Roles that require advanced knowledge of neural networks, model training pipelines, or large-scale inference systems are well-supported by the specialty occupation definition, particularly when paired with a graduate degree.
Does my degree field matter for H-1B sponsorship in AI and ML roles?
It matters significantly. Computer science, electrical engineering, applied mathematics, and statistics are the strongest degree fields for ML engineering petitions. A degree in a loosely related field, such as economics or business analytics, can create complications during adjudication if the employer's attorney can't draw a direct line between the coursework and the specific ML role. A master's or PhD in a core quantitative discipline removes most of the risk.
Are AI and ML engineering roles subject to the H-1B lottery?
Most are, unless you're working for a qualifying cap-exempt employer. Private tech companies, startups, and most corporations file cap-subject H-1B visa petitions that require selection in the annual lottery. Universities, affiliated nonprofit research organizations, and certain government research entities are cap-exempt and can sponsor H-1B workers year-round. If lottery timing is a concern, filtering for research-oriented ML roles at institutions is worth considering.
What types of employers sponsor the most AI and ML engineering roles?
Large technology firms account for the largest volume of ML engineering LCA filings, but mid-size AI-focused companies, financial institutions building quantitative models, and healthcare technology firms have become increasingly active sponsors. Defense contractors and national labs also sponsor significant numbers of ML roles, often outside the cap. You can browse sponsoring employers across all of these categories on Migrate Mate, filtered specifically for roles open to visa sponsorship.
Can I transfer my H-1B to a new AI or ML engineering role without losing my place in line?
Yes, H-1B portability allows you to start working for a new employer as soon as the transfer petition is filed, without waiting for approval, as long as you've been in valid H-1B status for at least 180 days. If you have an approved I-140 from a previous employer, you may also be able to retain your priority date when moving to a new ML role, which is significant given green card backlogs for employment-based categories.
What is the prevailing wage requirement for sponsored AI ML Engineering 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.