H-1B Visa AI ML Engineering Jobs
AI ML Engineering roles qualify for H-1B sponsorship as specialty occupations requiring at least a bachelor's degree in computer science, statistics, or a directly related field. Employers filing LCAs must certify a prevailing wage, and most active sponsors have multi-year H-1B filing histories you can verify before applying.
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Get Access To All JobsINTRODUCTION
This role is for a hands-on lead specializing in Edge AI deployment. The successful candidate will provide specialized expertise in model optimization at the edge, robust deployment, and MLOps pipeline development. You will leverage your skills in edge optimization, system and embedded knowledge, AI/machine learning, MLOps, computer vision, innovation, and problem solving to drive advanced AI solutions.
We are seeking someone with embedded AI experience, particularly with GPUs or AI accelerators, and strong system solution knowledge for AI application deployment.
You will be part of the Forge and AI team, based in Atlanta, Georgia.
BASIC QUALIFICATIONS
- Proven experience in embedded AI with hands-on expertise in GPU or AI accelerator deployment.
- Strong skills in edge model optimization and embedded system architecture.
- Deep understanding of AI/machine learning algorithms and computer vision applications.
- Experience building and managing MLOps pipelines for AI model deployment at the edge.
- Excellent problem-solving skills and a passion for innovation in embedded AI technologies.
PREFERRED QUALIFICATIONS
- Background in system solutions with AI application deployment experience.
- Strong knowledge of embedded systems and real-time operating environments.
- Experience working in collaborative, agile environments.
- Advanced degree in Computer Science, Electrical Engineering, or related technical field preferred.
KEY RESPONSIBILITIES
- Lead hands-on development and deployment of Edge AI solutions with a focus on model optimization and performance on embedded platforms.
- Design and implement robust MLOps pipelines to support continuous integration and deployment of AI models at the edge.
- Collaborate with cross-functional teams to integrate AI applications into embedded systems using GPUs and AI accelerators.
- Provide technical leadership and mentorship in embedded AI, system architecture, and AI deployment strategies.
- Drive innovation and problem-solving initiatives to enhance AI capabilities and deployment robustness on edge devices.
Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.
INTRODUCTION
This role is for a hands-on lead specializing in Edge AI deployment. The successful candidate will provide specialized expertise in model optimization at the edge, robust deployment, and MLOps pipeline development. You will leverage your skills in edge optimization, system and embedded knowledge, AI/machine learning, MLOps, computer vision, innovation, and problem solving to drive advanced AI solutions.
We are seeking someone with embedded AI experience, particularly with GPUs or AI accelerators, and strong system solution knowledge for AI application deployment.
You will be part of the Forge and AI team, based in Atlanta, Georgia.
BASIC QUALIFICATIONS
- Proven experience in embedded AI with hands-on expertise in GPU or AI accelerator deployment.
- Strong skills in edge model optimization and embedded system architecture.
- Deep understanding of AI/machine learning algorithms and computer vision applications.
- Experience building and managing MLOps pipelines for AI model deployment at the edge.
- Excellent problem-solving skills and a passion for innovation in embedded AI technologies.
PREFERRED QUALIFICATIONS
- Background in system solutions with AI application deployment experience.
- Strong knowledge of embedded systems and real-time operating environments.
- Experience working in collaborative, agile environments.
- Advanced degree in Computer Science, Electrical Engineering, or related technical field preferred.
KEY RESPONSIBILITIES
- Lead hands-on development and deployment of Edge AI solutions with a focus on model optimization and performance on embedded platforms.
- Design and implement robust MLOps pipelines to support continuous integration and deployment of AI models at the edge.
- Collaborate with cross-functional teams to integrate AI applications into embedded systems using GPUs and AI accelerators.
- Provide technical leadership and mentorship in embedded AI, system architecture, and AI deployment strategies.
- Drive innovation and problem-solving initiatives to enhance AI capabilities and deployment robustness on edge devices.
Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.
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Get Access To All JobsTips for Finding H-1B Visa Sponsorship in AI ML Engineering
Align your degree to the SOC code
USCIS adjudicators scrutinize whether your degree field directly supports an AI ML Engineering role. Check O*NET for the SOC code your employer uses, a mismatch between your major and that code is a common RFE trigger.
Verify prevailing wage before accepting offers
Run the OFLC Wage Search using the employer's job location and SOC code before you negotiate. If the offered salary falls below the Level I or II prevailing wage, the LCA won't certify and the petition stalls.
Search employers by LCA filing history on Migrate Mate
Filter AI ML Engineering roles on Migrate Mate by verified LCA filing history so you're only applying to employers with a documented track record of H-1B sponsorship, not just a careers page that says 'visa sponsorship available.'
Request a specialty occupation support letter early
AI ML Engineering duties can span multiple SOC codes. Ask your employer to draft a detailed specialty occupation support letter before filing, USCIS increasingly issues RFEs on AI roles where job duties read as generalist software work.
Confirm cap-exempt employer status if lottery risk is a concern
Universities, nonprofit research organizations, and government research entities are cap-exempt, meaning no lottery. If you're open to those environments, USCIS defines the cap-exempt categories clearly, and AI research roles at those institutions qualify.
AI ML Engineering jobs are hiring across the US. Find yours.
Find AI ML Engineering JobsAI ML Engineering H-1B Visa: Frequently Asked Questions
Do AI ML Engineering roles qualify as H-1B specialty occupations?
Yes. AI ML Engineering meets the specialty occupation standard because the role normally requires at least a bachelor's degree in computer science, mathematics, statistics, or a directly related field. USCIS evaluates the actual job duties, not just the title, so an employer's job description needs to reflect degree-level theoretical and applied work, not just tool operation or prompt engineering without a technical underpinning.
Which degree fields support an H-1B petition for an AI ML Engineering position?
Computer science, electrical engineering, mathematics, statistics, and data science are the most straightforward. Degrees in physics or operations research can also qualify if the job duties align. A degree in an unrelated field, even with extensive self-taught ML experience, creates specialty occupation problems, USCIS looks at the degree-job nexus, and a weak match is one of the most common RFE grounds for AI roles.
How do I find employers who actually sponsor H-1B visas for AI ML Engineering roles?
Migrate Mate aggregates employer LCA filing data from DOL disclosure files, so you can see which companies have filed H-1B petitions for AI and ML job titles specifically, not just general software engineering roles. This filters out employers who claim to sponsor but have no filing history, which is common in early-stage startups that haven't yet navigated the process.
What is the prevailing wage requirement for H-1B AI ML Engineering roles and who sets it?
The DOL sets prevailing wages through the OFLC Wage Search tool, which returns four wage levels for a given SOC code and geographic area. Your employer must pay at least the prevailing wage for your level to get the LCA certified. AI ML Engineering roles in major tech hubs typically fall under SOC 15-2051 (Data Scientists) or 15-1252 (Software Developers), and the wage level assigned affects both your offer negotiation and the petition's compliance.
Can I switch H-1B employers if I already work in AI ML Engineering at one company?
Yes, H-1B portability under AC21 lets you start working for a new employer as soon as they file an H-1B transfer petition, without waiting for approval, as long as your current H-1B has been approved for at least 180 days. The new employer files a new I-129 with a fresh LCA certified for that role and location. Your specialty occupation documentation carries over in concept, but the new employer must independently support the petition.
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