Machine Learning Jobs in USA with Visa Sponsorship
Machine learning roles in the US span academic research labs, big tech research divisions, and applied teams at companies across every industry, all of which regularly sponsor international talent. A strong publication record, conference presentations at venues like NeurIPS or ICML, and demonstrated research contributions significantly strengthen both your job applications and your visa petition. The field rewards deep expertise in areas like deep learning, probabilistic modeling, or optimization, making it one of the most accessible paths for researchers seeking US sponsorship. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsINTRODUCTION
Imagine what you could do here. At Apple, revolutionary ideas have a way of becoming extraordinary products, services, and customer experiences. Join the Ai and Data Platforms - Applied Machine Learning team to pioneer enterprise products where generative AI meets Apple's unique commitment to privacy-first innovation. Together, we'll create tools that redefine industries while safeguarding what matters most - our users' trust.
DESCRIPTION
As a Sr Machine Learning Manager on the Applied Machine Learning team, you will lead a team of ML engineers building SOTA AI models and scalable APIs that power features critical for Apple’s enterprise generative AI efforts.
You'll be responsible for the technical execution and delivery of AI systems that span the AI stack - from SOTA models and inference pipelines to developer-facing APIs and service integrations. This role requires someone who deeply understands both AI/ML fundamentals and engineering principles, able to make informed decisions about how to build systems that support and push the boundaries of modern AI/ML techniques.
If you have a deep understanding for AI/ML fundamentals, deep learning, generative AI, strong engineering background and leadership experience, then this role may be a great one for you! In this role you will lead and mentor forward-thinking engineers and will own related activities and interactions with teams across Apple. You will work cross-functionally with key internal partners, researchers, product teams, and platform engineers for roadmap development and delivery of our products.
Responsibilities
- Actively manage performance of the team reporting to you and take responsibility to drive its outcome.
- Build production AI-first software, translating research innovations into shippable, scalable systems.
- Ensure products meet Apple's standards for performance, reliability, scalability, privacy, and user experience.
- Guide development of APIs and frameworks for agentic workflows and/or complex multi-step ML systems.
- Own details of projects and long-term vision for the team.
- Collaborate optimally with partner/product teams to set the goals, expectations, and roadmaps for their teams.
- Communicate technical details and roadmaps for diverse audiences including engineering partners, product management teams, and executives across Apple.
MINIMUM QUALIFICATIONS
- 6+ years of overall experience in ML engineering, ML science, or related fields, with 3+ years in technical leadership or management roles.
- Deep understanding of AI/ML and gen AI fundamentals including neural network architectures, attention mechanisms, modern agentic workflows, multi-step reasoning systems.
- Strong software engineering fundamentals and understanding of API design for complex ML applications.
- Track record of leading teams and delivering complex AI/ML projects from conception to production.
- Strong communication skills.
- BS in Computer Science, Machine Learning, or related field (or equivalent industry experience).
PREFERRED QUALIFICATIONS
- MS, or PhD in Computer Science, Machine Learning, or related field (or equivalent industry experience).
- Exceptional interpersonal and communication skills, with a proven ability to translate complex technical concepts and roadmaps for diverse audiences, including partner teams, and executive leadership.
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 $228,100 and $393,800, 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.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
How to Get Visa Sponsorship in Machine Learning
Leverage top-tier publications for an O-1 visa
Papers accepted at NeurIPS, ICML, ICLR, or CVPR carry significant weight in an O-1 extraordinary ability petition. Combine publications with citation counts, peer review invitations, and conference keynotes to build a compelling case.
Explore EB-2 NIW if you hold a PhD in ML
ML researchers with doctoral degrees can petition for an EB-2 National Interest Waiver without employer sponsorship. If your work has applications in healthcare, climate, defense, or other areas of national importance, NIW may be a viable green card path.
Target industries beyond big tech
ML talent is in demand in quantitative finance (Two Sigma, Citadel, DE Shaw), pharmaceuticals (Pfizer, Genentech), and autonomous vehicles (Waymo). These industries sponsor aggressively and often pay competitively with major tech companies.
Use cap-exempt positions at national labs
Sandia, Los Alamos, and Oak Ridge National Laboratories run active ML research programs with H-1B cap-exempt positions. No lottery required, and you can file any time of year while working on cutting-edge problems.
Build a strong STEM OPT runway
CS, mathematics, and statistics degrees qualify for STEM OPT - up to 3 years of work authorization. Use that time to publish, contribute to production ML systems, and establish the track record that makes your employer invest in long-term sponsorship.
Highlight your engineering skills alongside research
Employers sponsoring ML roles want candidates who can move models from notebooks to production. Proficiency in PyTorch, distributed training, and ML infrastructure makes you more valuable and strengthens the case for a technical specialty occupation.
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Get Access To All JobsFrequently Asked Questions
How important is a publication record for getting sponsored in a machine learning role?
Publications are highly important for research-focused ML roles at organizations like Google DeepMind, Meta FAIR, or university labs. For H-1B purposes, they demonstrate specialized knowledge at the level expected of a degree holder. For O-1 petitions, publications are one of the core criteria for extraordinary ability. Applied ML roles at companies focused on deploying existing techniques may prioritize engineering skills over publications, so the importance depends on whether the role is research or production-oriented.
Can I transition from academia to an industry ML role in the U.S., and will employers sponsor that transition?
Yes. The academic-to-industry transition is one of the most well-established paths in machine learning. Companies like Google, Meta, Microsoft, and Amazon have research scientist roles specifically designed for people with academic backgrounds and routinely sponsor H-1B and O-1 visas for these hires. If you are currently a postdoc or researcher at a U.S. university, you may already have J-1 or H-1B status that can be transferred to an industry employer. Connect your academic work to practical applications during interviews to show awareness of production constraints.
Does the O-1 visa work well for ML researchers?
Yes, the O-1A is one of the strongest visa pathways for ML researchers with solid track records. Published papers at top venues (NeurIPS, ICML, ICLR, CVPR), peer review service for journals and conferences, high citation counts, and significant open-source contributions can all serve as evidence of extraordinary ability. The O-1 has no annual cap and no lottery, and it can be processed in 15 business days with premium processing. Many top AI labs actively support O-1 applications for research hires.
Which ML specializations are most in demand for visa sponsorship?
Large language models, reinforcement learning, and computer vision remain the highest-demand specializations. Emerging areas like geometric deep learning, causal ML, and efficient model architectures are particularly valuable for immigration purposes because the talent pool is extremely small. USCIS evaluates whether the role requires someone with your specific expertise, and niche specializations make that argument easier. Applied specializations like recommendation systems, search ranking, and fraud detection are also heavily sponsored.
Do open-source contributions to ML libraries help with visa petitions?
Yes, particularly for O-1 petitions where they can serve as evidence of original contributions of major significance to the field. Contributions to widely used frameworks like PyTorch, TensorFlow, Hugging Face Transformers, or scikit-learn carry the most weight. Document your contributions with metrics: download counts, GitHub stars, citations in papers, and adoption by major companies. For H-1B petitions, open-source work is less directly relevant but helps demonstrate the specialized depth of your expertise.
What is the prevailing wage requirement for sponsored Machine Learning jobs?
When a U.S. employer sponsors a foreign worker for a work visa, they are legally required to pay at least the "prevailing wage", the average wage paid to workers in the same occupation, in the same geographic area, with similar experience. This is set by the Department of Labor to prevent employers from hiring foreign workers at below-market rates. The prevailing wage varies significantly by role, location, and experience level. For example, a machine learning in California will have a different prevailing wage than the same role in a smaller state. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search Page.
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