AI ML Engineer Jobs in USA with Visa Sponsorship
AI ML Engineer jobs draw consistent H-1B visa sponsorship because the role qualifies as a specialty occupation under USCIS rules, requiring at least a bachelor's degree in computer science, mathematics, or a related field. Employers file a Labor Condition Application before sponsoring, and many also support OPT, STEM OPT, and green card pathways for strong candidates. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsYou have a clear vision of where your career can go. And we have the leadership to help you get there. At CNA, we strive to create a culture in which people know they matter and are part of something important, ensuring the abilities of all employees are used to their fullest potential.
An individual contributor role that provides a high level of leadership and expertise in building Artificial Intelligence and Machine Learning (AI & ML) platforms and solutions for financial services and insurance. The role will be responsible for developing data into usable information to create automated analytics solutions.
JOB DESCRIPTION:
Essential Duties & Responsibilities
Performs a combination of duties in accordance with departmental guidelines:
- Build, apply, and promote adoption of platform-wide standards, reusable design patterns, and golden-path templates that enable product and feature teams to build, deploy, and operate AI solutions safely and consistently.
- Drive end-to-end delivery of new platform capabilities, from technical discovery and design through prototyping, hardening, and full production rollout, with hands-on involvement at critical technical milestones to ensure quality and coherence.
- Build and operationalize the core platform service catalog, including LLM gateway and routing layers, prompt lifecycle management, agentic orchestration frameworks, Retrieval-Augmented Generation (RAG) pipelines, vector stores, model registries, and rigorous automated evaluation infrastructure.
- Build and maintain robust CI/CD and AIOps pipelines for AI systems, incorporating automated evaluation gates, model and data versioning controls, staged deployment promotion, and continuous cost and performance optimization guardrails.
- Implement enterprise-grade multi-agent and single-agent workflow patterns for high-value business use cases, following clear standards for orchestration design, state and memory management, tool and API integration, and safe autonomy controls, including human-in-the-loop approvals, permission scoping, and comprehensive audit trails.
- Design and implement knowledge grounding systems, including hybrid retrieval strategies, semantic reranking, ontology-driven entity modeling, and knowledge graph integration, to measurably improve AI output accuracy, explainability, traceability, and readiness for regulatory audit.
- Embed responsible AI and compliance-by-design principles across the platform, covering data privacy protections, enterprise secrets management, granular access controls, output leakage prevention, and model risk governance practices aligned to enterprise and regulatory standards.
- Participate in code and design reviews, and contribute to testing, documentation, and operational readiness.
May perform additional duties as assigned.
Reporting Relationship
Typically Director or above.
Skills, Knowledge & Abilities
- Deep AI platform and AIOps engineering expertise, with hands-on experience building, deploying, and operating shared AI platform capabilities such as model serving layers, LLM gateway and proxy services, prompt registries, vector databases, and automated evaluation harnesses at enterprise scale.
- Proven agentic system design capability, with hands-on experience building multi-agent and single-agent workflow systems using orchestration frameworks such as LangGraph and Google ADK, including tool and function calling patterns, state and memory persistence strategies, and robust safe autonomy controls.
- Applied GenAI depth spanning LLM solution architecture patterns, model selection and routing strategies, advanced prompt engineering techniques, fine-tuning and RLHF tradeoffs, and production-grade RAG and hybrid retrieval system design and optimization.
- Strong cloud-native and distributed systems skills, with deep GCP expertise across Vertex AI, Cloud Run, GKE, Pub/Sub, and BigQuery, and a solid command of API and service-based design, event-driven architecture, and high-availability and fault-tolerant system patterns.
- Knowledge grounding and semantic layer proficiency, including experience building canonical ontology and entity models, designing vector search and hybrid retrieval pipelines, integrating knowledge graphs, implementing reranking strategies, and establishing citation and traceability mechanisms that support explainability and compliance.
- Solid AIOps and platform reliability engineering experience, including CI/CD pipeline design for AI systems, automated evaluation and quality gates, model and dataset versioning, production monitoring and observability, reliability engineering practices, and systematic cost-performance optimization.
- Practical responsible AI and security expertise, with experience implementing enterprise AI governance frameworks, model risk management practices, PII and data privacy controls, audit and event logging, and compliance-by-design patterns suited to regulated industries.
- Strong SDLC and hands-on engineering fundamentals, including Python proficiency, architectural and code review practices, comprehensive testing strategies for AI systems, technical debt management, refactoring discipline, and operational readiness standards.
- Familiarity with Scaled Agile (SAFe), including contributing to Enabler Epics and PI planning.
- Strong communication skills, with the ability to collaborate with engineers, product partners, and business stakeholders, negotiate technology tradeoffs, mentor junior engineers, and explain technical concepts clearly to non-technical audiences.
Education & Experience
- Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or equivalent required; Master’s degree in AI, Machine Learning, Data Science, or a related discipline strongly preferred.
- 8+ years in software engineering and technical delivery, including work on large-scale, distributed enterprise systems across the full SDLC, from inception through production operations.
- 5+ years hands-on designing, delivering, and operating AI/ML or GenAI platform capabilities in production, with measurable outcomes in quality, reliability, and developer adoption.
- Strong Python proficiency and deep practical GCP experience (Vertex AI, GCP Agent Builder, and Gemini), with the ability to do hands-on technical work as part of the engineering team.
- Prior experience in regulated industries (insurance, financial services, or healthcare) strongly preferred, given stringent governance, auditability, and model risk management requirements.
- Consulting or enterprise delivery background is a plus, bringing structured problem-solving and stakeholder management.
LI-KJ1 #LI-HYBRID
In certain jurisdictions, CNA is legally required to include a reasonable estimate of the compensation for this role. In District of Columbia, California, Colorado, Connecticut, Illinois, Maryland, Massachusetts, New York and Washington, the national base pay range for this job level is $72,000 to $141,000 annually. Salary determinations are based on various factors, including but not limited to, relevant work experience, skills, certifications and location. CNA offers a comprehensive and competitive benefits package to help our employees – and their family members – achieve their physical, financial, emotional and social wellbeing goals. For a detailed look at CNA’s benefits, please visit cnabenefits.com.
CNA utilizes AI-enabled technology during the recruiting process. For more information, please visit our careers page.
CNA is committed to providing reasonable accommodations to qualified individuals with disabilities in the recruitment process. To request an accommodation, please contact leaveadministration@cna.com.
You have a clear vision of where your career can go. And we have the leadership to help you get there. At CNA, we strive to create a culture in which people know they matter and are part of something important, ensuring the abilities of all employees are used to their fullest potential.
An individual contributor role that provides a high level of leadership and expertise in building Artificial Intelligence and Machine Learning (AI & ML) platforms and solutions for financial services and insurance. The role will be responsible for developing data into usable information to create automated analytics solutions.
JOB DESCRIPTION:
Essential Duties & Responsibilities
Performs a combination of duties in accordance with departmental guidelines:
- Build, apply, and promote adoption of platform-wide standards, reusable design patterns, and golden-path templates that enable product and feature teams to build, deploy, and operate AI solutions safely and consistently.
- Drive end-to-end delivery of new platform capabilities, from technical discovery and design through prototyping, hardening, and full production rollout, with hands-on involvement at critical technical milestones to ensure quality and coherence.
- Build and operationalize the core platform service catalog, including LLM gateway and routing layers, prompt lifecycle management, agentic orchestration frameworks, Retrieval-Augmented Generation (RAG) pipelines, vector stores, model registries, and rigorous automated evaluation infrastructure.
- Build and maintain robust CI/CD and AIOps pipelines for AI systems, incorporating automated evaluation gates, model and data versioning controls, staged deployment promotion, and continuous cost and performance optimization guardrails.
- Implement enterprise-grade multi-agent and single-agent workflow patterns for high-value business use cases, following clear standards for orchestration design, state and memory management, tool and API integration, and safe autonomy controls, including human-in-the-loop approvals, permission scoping, and comprehensive audit trails.
- Design and implement knowledge grounding systems, including hybrid retrieval strategies, semantic reranking, ontology-driven entity modeling, and knowledge graph integration, to measurably improve AI output accuracy, explainability, traceability, and readiness for regulatory audit.
- Embed responsible AI and compliance-by-design principles across the platform, covering data privacy protections, enterprise secrets management, granular access controls, output leakage prevention, and model risk governance practices aligned to enterprise and regulatory standards.
- Participate in code and design reviews, and contribute to testing, documentation, and operational readiness.
May perform additional duties as assigned.
Reporting Relationship
Typically Director or above.
Skills, Knowledge & Abilities
- Deep AI platform and AIOps engineering expertise, with hands-on experience building, deploying, and operating shared AI platform capabilities such as model serving layers, LLM gateway and proxy services, prompt registries, vector databases, and automated evaluation harnesses at enterprise scale.
- Proven agentic system design capability, with hands-on experience building multi-agent and single-agent workflow systems using orchestration frameworks such as LangGraph and Google ADK, including tool and function calling patterns, state and memory persistence strategies, and robust safe autonomy controls.
- Applied GenAI depth spanning LLM solution architecture patterns, model selection and routing strategies, advanced prompt engineering techniques, fine-tuning and RLHF tradeoffs, and production-grade RAG and hybrid retrieval system design and optimization.
- Strong cloud-native and distributed systems skills, with deep GCP expertise across Vertex AI, Cloud Run, GKE, Pub/Sub, and BigQuery, and a solid command of API and service-based design, event-driven architecture, and high-availability and fault-tolerant system patterns.
- Knowledge grounding and semantic layer proficiency, including experience building canonical ontology and entity models, designing vector search and hybrid retrieval pipelines, integrating knowledge graphs, implementing reranking strategies, and establishing citation and traceability mechanisms that support explainability and compliance.
- Solid AIOps and platform reliability engineering experience, including CI/CD pipeline design for AI systems, automated evaluation and quality gates, model and dataset versioning, production monitoring and observability, reliability engineering practices, and systematic cost-performance optimization.
- Practical responsible AI and security expertise, with experience implementing enterprise AI governance frameworks, model risk management practices, PII and data privacy controls, audit and event logging, and compliance-by-design patterns suited to regulated industries.
- Strong SDLC and hands-on engineering fundamentals, including Python proficiency, architectural and code review practices, comprehensive testing strategies for AI systems, technical debt management, refactoring discipline, and operational readiness standards.
- Familiarity with Scaled Agile (SAFe), including contributing to Enabler Epics and PI planning.
- Strong communication skills, with the ability to collaborate with engineers, product partners, and business stakeholders, negotiate technology tradeoffs, mentor junior engineers, and explain technical concepts clearly to non-technical audiences.
Education & Experience
- Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or equivalent required; Master’s degree in AI, Machine Learning, Data Science, or a related discipline strongly preferred.
- 8+ years in software engineering and technical delivery, including work on large-scale, distributed enterprise systems across the full SDLC, from inception through production operations.
- 5+ years hands-on designing, delivering, and operating AI/ML or GenAI platform capabilities in production, with measurable outcomes in quality, reliability, and developer adoption.
- Strong Python proficiency and deep practical GCP experience (Vertex AI, GCP Agent Builder, and Gemini), with the ability to do hands-on technical work as part of the engineering team.
- Prior experience in regulated industries (insurance, financial services, or healthcare) strongly preferred, given stringent governance, auditability, and model risk management requirements.
- Consulting or enterprise delivery background is a plus, bringing structured problem-solving and stakeholder management.
LI-KJ1 #LI-HYBRID
In certain jurisdictions, CNA is legally required to include a reasonable estimate of the compensation for this role. In District of Columbia, California, Colorado, Connecticut, Illinois, Maryland, Massachusetts, New York and Washington, the national base pay range for this job level is $72,000 to $141,000 annually. Salary determinations are based on various factors, including but not limited to, relevant work experience, skills, certifications and location. CNA offers a comprehensive and competitive benefits package to help our employees – and their family members – achieve their physical, financial, emotional and social wellbeing goals. For a detailed look at CNA’s benefits, please visit cnabenefits.com.
CNA utilizes AI-enabled technology during the recruiting process. For more information, please visit our careers page.
CNA is committed to providing reasonable accommodations to qualified individuals with disabilities in the recruitment process. To request an accommodation, please contact leaveadministration@cna.com.
See all 15,412+ AI ML Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new AI ML Engineer roles.
Get Access To All JobsTips for Finding Visa Sponsorship as an AI ML Engineer
Align your degree to the role
USCIS requires a direct relationship between your degree field and the ML Engineer role. A degree in computer science, statistics, or mathematics maps cleanly. A business or unrelated STEM degree raises specialty occupation questions, so prepare a credential evaluation letter upfront.
Use OFLC Wage Search before negotiating
Your employer must pay the prevailing wage certified on the LCA. Pull the OFLC Wage Search for your job title and metro area before you negotiate, so your offer clears the DOL threshold and avoids an LCA amendment after filing.
Target companies with active LCA filing history
Find AI ML Engineer roles at employers with verified sponsorship history on Migrate Mate, which surfaces companies that have filed LCAs for this role, so you're applying where sponsorship is already established practice, not a first-time ask.
Front-load your STEM OPT timeline
If you're on F-1 OPT, your employer must be enrolled in E-Verify before your STEM OPT extension is approved. Confirm E-Verify enrollment during offer negotiations, not after, since late enrollment delays your 24-month extension start date.
Distinguish ML research from ML engineering in your petition
H-1B petitions for AI ML Engineers can attract RFEs if the job duties read as general software development. Your offer letter and LCA should specify model training, deployment pipelines, or algorithm design to anchor the specialty occupation argument clearly.
Start PERM early for EB-2 or EB-3 sponsorship
If your employer plans to sponsor a green card, the PERM labor certification process runs six to twelve months before USCIS even sees the petition. Ask HR about starting PERM during your second year of H-1B status to avoid gaps caused by priority date backlogs.
Frequently Asked Questions
Does an AI ML Engineer role qualify as a specialty occupation for H-1B purposes?
Yes, in most cases. USCIS treats AI ML Engineer as a specialty occupation because the role normally requires at least a bachelor's degree in computer science, statistics, applied mathematics, or a closely related field. Petitions that describe duties limited to general software development without machine learning specificity have drawn Requests for Evidence, so the job description in your LCA and I-129 needs to reflect actual ML work such as model development, training pipelines, or deployment infrastructure.
Which visa types do employers most commonly use to sponsor AI ML Engineers?
H-1B is the dominant path because the role meets the specialty occupation standard. F-1 students often bridge to full sponsorship through OPT and the 24-month STEM OPT extension, which requires the employer to be E-Verify enrolled and to sign a training plan with USCIS. Australian nationals can use the E-3 visa, which has a separate annual cap that has never been exhausted and does not use a lottery. TN visa status is not available for AI ML Engineer roles, as the position does not appear on the USMCA profession list.
Can I find AI ML Engineer jobs with confirmed sponsorship history without researching each employer manually?
Migrate Mate lists AI ML Engineer roles at employers with verified LCA sponsorship history, so you can browse opportunities where sponsorship is already documented rather than guessing based on company size or industry. This saves time and helps you avoid applying to roles where the employer has never filed for this occupation.
What happens to my work authorization if my H-1B petition is filed but not yet approved?
If you're already in H-1B status, you can start working for a new employer as soon as the new I-129 petition is filed, without waiting for USCIS approval. This portability rule applies because you're maintaining valid H-1B status throughout. If you're transitioning from OPT or STEM OPT and your petition is filed before your authorization expires, the cap-gap rule may extend your work authorization through September 30 of that year.
Does a master's degree in an adjacent field like data science or electrical engineering satisfy the specialty occupation requirement for this role?
It can, but the connection between your degree field and the specific ML Engineer duties must be defensible. USCIS looks at whether the degree curriculum provided the theoretical and applied foundation the role requires. A master's in data science maps closely. Electrical engineering with signal processing or optimization coursework can also qualify, particularly for roles involving edge ML or hardware-adjacent model deployment. Having a credential evaluation from a recognized agency strengthens the petition if your degree title is not an obvious match.
What is the prevailing wage requirement for sponsored AI ML Engineer 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.