Machine Learning Jobs at Google with Visa Sponsorship
Machine Learning jobs at Google involve building the models and infrastructure that power products used by billions of people worldwide. The company sponsors H-1B visa, H-1B1 visa, and E-3 visas for qualified ML engineers and researchers, and its in-house immigration team is experienced handling sponsorship across all seniority levels.
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Get Access To All JobsINTRODUCTION
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
LiteRT is Google’s next generation on-device AI framework, succeeding TensorFlow Lite (TFLite). It is designed to maximize the performance, efficiency, and portability of ML models on a wide array of edge devices, from mobile phones to embedded systems. LiteRT significantly upgrades GPU acceleration and introduces native NPU acceleration, while maintaining and enhancing the robust CPU performance inherited from TFLite.
LiteRT enables developers and Google products to deploy AI across mobile, web, desktop, and embedded. Our team focuses on building cross-platform infrastructure aligned with Google's business needs, serving top Google products (Android, Chrome, Photos, Meet, YouTube, etc.), third-party developers, and specialized Pixel solutions. Our goal is to provide on-device AI infrastructure with exceptional performance, enabling framework and device flexibility at scale.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
ROLE AND RESPONSIBILITIES
- Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.
- Develop LiteRT, Google's on-device AI framework for first- and third-party, enabling SOTA hardware acceleration and use cases on edge platforms.
- Enable on-device deployment of key models, such as Gemini Nano and Gemma, across various accelerators (GPU/Pixel TPU/NPUs/CPU) on Android, Chrome, iOS, desktop, and more.
- Improve performance of on-device model inference via optimizations in the model representation, on-device runtime and kernel implementation.
MINIMUM QUALIFICATIONS
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
- 2 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- Experience with runtimes and performance tuning.
- Experience in mobile development.
PREFERRED QUALIFICATIONS
- Master's degree or PhD in Computer Science or related technical fields.
- Experience in leading and delivering successful ML projects focused on on-device deployment (Android, iOS, web browsers, or embedded devices).
- Experience in ML frameworks (e.g., PyTorch, JAX, TensorFlow).
- Experience with on-device ML SDKs/tooling (e.g., TensorFlow Lite, ExecuTorch, Core ML, SNPE/QNN).
- Strong understanding of Generative AI model architectures and their optimization for on-device execution.
- Passion for innovation and a strong desire to push the boundaries of what's possible with on-device ML.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
INTRODUCTION
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
LiteRT is Google’s next generation on-device AI framework, succeeding TensorFlow Lite (TFLite). It is designed to maximize the performance, efficiency, and portability of ML models on a wide array of edge devices, from mobile phones to embedded systems. LiteRT significantly upgrades GPU acceleration and introduces native NPU acceleration, while maintaining and enhancing the robust CPU performance inherited from TFLite.
LiteRT enables developers and Google products to deploy AI across mobile, web, desktop, and embedded. Our team focuses on building cross-platform infrastructure aligned with Google's business needs, serving top Google products (Android, Chrome, Photos, Meet, YouTube, etc.), third-party developers, and specialized Pixel solutions. Our goal is to provide on-device AI infrastructure with exceptional performance, enabling framework and device flexibility at scale.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
ROLE AND RESPONSIBILITIES
- Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.
- Develop LiteRT, Google's on-device AI framework for first- and third-party, enabling SOTA hardware acceleration and use cases on edge platforms.
- Enable on-device deployment of key models, such as Gemini Nano and Gemma, across various accelerators (GPU/Pixel TPU/NPUs/CPU) on Android, Chrome, iOS, desktop, and more.
- Improve performance of on-device model inference via optimizations in the model representation, on-device runtime and kernel implementation.
MINIMUM QUALIFICATIONS
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
- 2 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- Experience with runtimes and performance tuning.
- Experience in mobile development.
PREFERRED QUALIFICATIONS
- Master's degree or PhD in Computer Science or related technical fields.
- Experience in leading and delivering successful ML projects focused on on-device deployment (Android, iOS, web browsers, or embedded devices).
- Experience in ML frameworks (e.g., PyTorch, JAX, TensorFlow).
- Experience with on-device ML SDKs/tooling (e.g., TensorFlow Lite, ExecuTorch, Core ML, SNPE/QNN).
- Strong understanding of Generative AI model architectures and their optimization for on-device execution.
- Passion for innovation and a strong desire to push the boundaries of what's possible with on-device ML.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
See all 53+ Machine Learning Jobs at Google
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Jobs at Google.
Get Access To All JobsTips for Finding Machine Learning Jobs at Google
Align your portfolio to Google's ML stack
Google publishes research through Google DeepMind and Google Brain. Demonstrating familiarity with JAX, TensorFlow, or TPU-based training in your portfolio signals direct relevance to the teams most likely to sponsor you.
Target roles that specify research or production
Google separates Research Scientist, Software Engineer (ML), and ML Engineer tracks. Each has different sponsorship timelines internally. Applying to the track matching your background reduces the chance of a role reclassification mid-process.
Request cap-exempt status clarity before accepting
If you're transitioning from a university or nonprofit research role, confirm with the recruiter whether Google will file as a cap-exempt petitioner. This affects whether you can start immediately or must wait for the October 1 H-1B activation date.
Gather degree equivalency documentation early
Google's ML roles typically require a master's or PhD in a quantitative field. If your degree is from outside the U.S., obtain a credential evaluation from a NACES-approved evaluator before your offer stage so it's ready when USCIS reviews the petition.
Understand how E-3 and H-1B1 affect your offer timing
Australian and Chilean or Singaporean nationals can use the E-3 or H-1B1 visa pathways, which sit outside the annual H-1B cap and lottery. Google sponsors both, meaning you can potentially start sooner without waiting for an April registration window.
Find open ML roles at Google through Migrate Mate
Migrate Mate filters Google's open Machine Learning jobs by the visa types the company sponsors. Use it to identify current openings where sponsorship is confirmed rather than sifting through listings with no immigration clarity.
Frequently Asked Questions
Does Google sponsor H-1B visas for Machine Learning roles?
Yes, Google sponsors H-1B visas for Machine Learning engineers and researchers. The company has a dedicated in-house immigration team that manages petitions across all seniority levels, from new graduate hires to senior staff. If you receive an offer, Google will initiate the sponsorship process directly. For roles where you're already in H-1B status with another employer, Google can also file an H-1B transfer.
How do I apply for Machine Learning jobs at Google?
Applications go through Google's careers portal at careers.google.com. Search for roles using terms like 'Machine Learning Engineer,' 'Research Scientist,' or 'ML Infrastructure.' Google's process typically involves a recruiter screen, technical phone interviews focused on ML fundamentals and coding, and a virtual onsite covering systems design, ML theory, and behavioral components. Migrate Mate also lists Google's open ML roles filtered by visa sponsorship type, which makes it easier to confirm sponsorship eligibility before applying.
Which visa types does Google commonly sponsor for Machine Learning positions?
Google sponsors H-1B, H-1B1 visa, and E-3 visas for Machine Learning roles. The H-1B is the most common path and requires entry into the annual lottery for cap-subject candidates. H-1B1 is available to Chilean and Singaporean nationals, and the E-3 is available to Australian citizens. Both the H-1B1 and E-3 sit outside the H-1B cap, so they can move faster for eligible candidates.
What qualifications does Google expect for Machine Learning roles?
Most ML roles at Google list a master's or PhD in computer science, statistics, or a related quantitative discipline as a baseline. Practical experience with large-scale model training, familiarity with Google's open-source frameworks like TensorFlow or JAX, and a track record of applied or published research strengthen your candidacy significantly. For USCIS H-1B purposes, Google's positions are structured to meet the specialty occupation standard, but your degree must correspond directly to the ML field the role covers.
How do I think about the timeline from offer to visa approval at Google?
For H-1B cap-subject candidates, the timeline is tied to the annual registration window in March and an October 1 start date if selected. Google files petitions with premium processing in most cases, which brings USCIS adjudication to within 15 business days of receipt. E-3 and H-1B1 visa candidates generally move faster since there's no lottery. Factor in two to four weeks for the Labor Condition Application the employer files with DOL before the USCIS petition can be submitted.