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.
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WHAT YOU DO AT AMD CHANGES EVERYTHING
At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.
THE ROLE:
AMD is looking for an AI solutions validation Engineer who is passionate about complex AI solutions, AI infrastructure, building cluster scale automation for distributed training and inference workloads, MLOps. You will be a member of a core team of incredibly talented industry specialists and will work with the very latest hardware and software technology.
THE PERSON:
The ideal candidate should be passionate about software engineering, system design, validation, automation and possess leadership skills to drive sophisticated issues to resolution. Able to communicate effectively and work optimally with different teams across AMD.
KEY RESPONSIBILITIES:
- Work with AMD’s architecture specialists to validate AI solutions for distributed training and inference workloads with AMD's ROCM software
- Build cluster scale automation for distributed training and inference workloads
- Publish reference designs and benchmark numbers for AI workloads
- Apply a data minded approach to target optimization efforts
- Design and develop new groundbreaking AMD technologies
- Participate in new ASIC and hardware bring ups
- Develop technical relationships with peers and partners
PREFERRED EXPERIENCE:
- Good experience with complex compute systems used in AI, HPC deployments, backend network designs in RDMA clusters
- Experience in validating complex AI infrastructure - GPUs, networking, ROCEv2, UEC, running benchmark tests like IBPerf benchmarking, RCCL/NCCL
- Experience with running training of LLMs, MoE models, Image Generation, recommendations models with different frameworks like PyTorch, Tensorflow, Megatron-LM, JAX. Running training performance benchmarks
- Experience with running inference workloads in AI clusters with different inference frameworks like vLLM, SGLang. Running performance benchmarks for inference
- Experience with distributed systems and schedulers like Kubernetes, Slurm
- Ability to write high quality automation frameworks and scripts using Python or Golang
- Experience with performance profiling of CPUs, GPUs and debugging complex compute, network, storage problems
- Experience with AMD ROCM would be an added advantage
- Experience with Linux, Windows operating systems
- Effective communication and problem-solving skills
PREFERRED ACADEMIC CREDENTIALS:
Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent
QUALIFICATIONS
Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.
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.
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