Machine Learning Engineer Jobs at Google with Visa Sponsorship
Machine Learning Engineer jobs at Google sit at the intersection of research and production scale, covering everything from recommendation systems to large language models. Google has a consistent track record of sponsoring work visas for this function, and the process is handled through their in-house immigration team.
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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 Engineer Jobs at Google
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer Jobs at Google.
Get Access To All JobsTips for Finding Machine Learning Engineer Jobs at Google
Align your portfolio with Google's ML infrastructure
Google's ML hiring evaluates applied work on large-scale systems, not just academic research. Before applying, build public projects or papers that demonstrate experience with distributed training, model serving, or production ML pipelines at scale.
Target teams that match your visa category
Google sponsors H-1B, H-1B1 visa, and E-3 visas, but certain teams with government contracts may have restrictions on sponsored workers. Filter your application toward consumer, cloud, or research divisions where sponsorship eligibility is straightforward.
Time your application around H-1B cap deadlines
If you need H-1B sponsorship, Google submits registrations in March for the April lottery. Secure your offer and complete internal immigration paperwork well before February so the employer-side filing isn't rushed heading into cap season.
Prepare your degree equivalency documentation early
Google's immigration team may request a credential evaluation if your degree is from outside the U.S. For E-3 or H-1B petitions, USCIS reviews whether your field of study directly supports the ML Engineer role, so gather transcripts and an evaluation letter before your offer letter arrives.
Use Migrate Mate to find open ML Engineer roles at Google
Filtering for visa-sponsored roles manually across Google's careers portal is time-consuming. Migrate Mate surfaces Machine Learning Engineer openings at Google filtered by sponsorship type, so you apply only where your visa category is explicitly supported.
Clarify your status portability before accepting an offer
If you're on OPT and your H-1B is pending, confirm with Google's immigration counsel that your start date and STEM OPT extension create a continuous authorized period. A gap between OPT expiry and H-1B approval affects day-one eligibility.
Frequently Asked Questions
Does Google sponsor H-1B visas for Machine Learning Engineers?
Yes, Google sponsors H-1B visas for Machine Learning Engineers and has done so consistently across its engineering and research divisions. The process is managed through Google's in-house immigration team. If you're subject to the H-1B cap, Google submits registrations in March for the annual lottery, so your offer and internal paperwork need to be finalized well in advance.
How do I apply for Machine Learning Engineer jobs at Google?
Applications go through Google's careers portal at careers.google.com. Search for Machine Learning Engineer roles and filter by location. Google's ML hiring process typically involves a recruiter screen, technical phone interviews covering ML fundamentals and coding, and a virtual or onsite loop with system design and ML-specific components. Tailoring your resume to reflect production ML experience, not just research, improves your chances of clearing the initial screen.
Which visa types does Google commonly sponsor for Machine Learning Engineers?
Google sponsors H-1B, H-1B1 visa, and E-3 visas for Machine Learning Engineers. H-1B is the most common path for candidates from countries not covered by the other categories. H-1B1 is available to Chilean and Singaporean nationals, and E-3 is exclusively for Australian citizens. Each has different annual caps, processing timelines, and renewal rules, so which visa applies depends on your nationality.
What qualifications does Google expect for Machine Learning Engineer roles?
Google's ML Engineer roles typically require a bachelor's degree or higher in computer science, statistics, or a related field, with practical experience in Python, TensorFlow or JAX, and large-scale model training. Research publications or contributions to open-source ML frameworks strengthen an application. For visa purposes, USCIS will assess whether your degree field directly supports the specialty occupation, so a directly relevant degree matters beyond just getting the offer.
How do I find Machine Learning Engineer roles at Google that sponsor my visa type?
Google lists roles across multiple locations and teams, but not every posting makes visa sponsorship eligibility obvious. Migrate Mate aggregates Machine Learning Engineer openings at Google and filters them by visa sponsorship type, so you can identify roles where H-1B, E-3, or H-1B1 visa sponsorship is supported without manually researching each posting. This saves significant time if you're working against an OPT deadline or grace period.