ML Engineer Jobs at Google with Visa Sponsorship
ML Engineer jobs at Google span research, applied science, and production infrastructure, with the company sponsoring H-1B visa, H-1B1 visa, and E-3 visas for qualified candidates. The company has an established immigration program that handles sponsorship in-house, making it one of the more navigable paths for international engineers in this field.
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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+ ML Engineer Jobs at Google
Sign up for free to unlock all listings, filter by visa type, and get alerts for new ML Engineer Jobs at Google.
Get Access To All JobsTips for Finding ML Engineer Jobs at Google
Align your portfolio to Google's ML stack
Google's ML hiring centers on TensorFlow, JAX, and large-scale distributed training. Your portfolio and resume should demonstrate hands-on experience with these frameworks, not just familiarity. Projects showing model optimization at scale get traction here.
Target teams actively publishing ML research
Google Brain, DeepMind, and Google Research publish prolifically. Referencing specific papers from these teams in your application or interview prep signals genuine alignment with the work, which matters more than a polished generic cover letter.
Distinguish your visa type before applying
Google sponsors H-1B, H-1B1 visa, and E-3 visas, and each has different timelines and filing requirements. If you're Australian, the E-3 avoids the H-1B lottery entirely. Know which category applies to you before your offer conversation starts.
Confirm sponsorship intent during the recruiter screen
Google's recruiters handle sponsorship questions directly. Ask explicitly whether the specific team and role are approved for your visa type. Some research-track roles have different internal approval workflows than product engineering positions.
Use Migrate Mate to find open ML Engineer roles at Google
Not every sponsoring role is easy to surface across general job boards. Migrate Mate filters Google's ML Engineer openings by visa type, so you can identify which positions align with your sponsorship category before applying.
Prepare for H-1B cap timing if you're not exempt
If you're not currently on OPT or another cap-exempt status, H-1B registration opens in March for an October 1 start. USCIS runs a lottery when registrations exceed the 85,000 cap, so build your job search timeline around that window.
Frequently Asked Questions
Does Google sponsor H-1B visas for ML Engineers?
Yes, Google sponsors H-1B visas for ML Engineers and has a dedicated immigration team that manages the process in-house. Sponsorship is tied to the specific role and team, so you'll want to confirm with your recruiter that the position you're pursuing is approved for H-1B sponsorship before the offer stage.
Which visa types does Google sponsor for ML Engineer roles?
Google sponsors H-1B, H-1B1 visa, and E-3 visas for ML Engineers. The H-1B is the most common path and applies to most nationalities. H-1B1 is available to citizens of Chile and Singapore, and the E-3 is exclusive to Australian citizens. Each visa has different filing timelines and renewal rules, so the right category depends on your nationality.
How do I apply for ML Engineer jobs at Google?
Applications go through Google's careers portal, but roles fill quickly and aren't always easy to filter by sponsorship eligibility. Migrate Mate aggregates Google's open ML Engineer positions and lets you browse by visa type, so you can identify relevant openings faster. A strong application typically includes a tailored resume, a GitHub portfolio demonstrating ML work, and preparation for Google's technical interview process, which includes coding rounds and ML system design.
What qualifications does Google expect for ML Engineer roles?
Most ML Engineer roles at Google require a bachelor's degree at minimum in computer science, electrical engineering, or a closely related field, with a master's or PhD common for research-oriented positions. Practically, Google's hiring bar emphasizes hands-on experience with large-scale ML systems, proficiency in Python and at least one deep learning framework, and the ability to work across research and production environments.
How do I time my job search around the H-1B filing process at Google?
USCIS opens H-1B registration each March for a cap-subject petition, with employment starting October 1 at the earliest. If you're on F-1 OPT, you can start before October 1 using the cap-gap provision. Google typically begins sponsorship paperwork after an offer is signed, so securing your offer before March gives the immigration team enough time to file during that registration window.