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 JobsMinimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 8 years of experience with one or more general purpose programming languages (e.g., Java, C/C++, or Python).
- 5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
Preferred qualifications:
- 8 years of experience with data structures and algorithms.
- 6 years of experience with Machine Learning (ML) or quality, working on recommendation systems.
- Experience in recommender systems, clustering algorithms, SQL, and deep model.
- Experience in C++, Dremel/F1, and TensorFlow.
- Experience in Research.
- Ability to drive quality projects end-to-end from design to implementation to eventual launch.
About the job
We are the core team responsible for Search Ads Personalization. Our personalization models and infrastructure permeate through every part of Search Ads - from Ad Retrieval, Ranking, Auction and Creative writing. As a result, our problems are challenging both in depth (understanding nuances of user's needs), and breadth (scale across entire Google Search user base). We work on modeling user preferences and tasks using a myriad of technologies embedding models, language models, as well as building real-time and large scale inference infrastructure.
Personalizing our Ads has proven to be extremely successful, and as a result, it is a key objective of entire Ads organization. Join us to be in the centre of this personalization revolution! Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.
The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Focus on personalization (P13N) in the Generative AI space (ads on AIM, personalized ads creatives).
- Collaborate across GenAI teams in Ads Quality and GDM to create and execute on a goal/roadmap to ensure that our users feel connected with our GenAI Ads.
- Work on writing ads creatives or ad explanations to account for user tasks and preferences.
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.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 8 years of experience with one or more general purpose programming languages (e.g., Java, C/C++, or Python).
- 5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
Preferred qualifications:
- 8 years of experience with data structures and algorithms.
- 6 years of experience with Machine Learning (ML) or quality, working on recommendation systems.
- Experience in recommender systems, clustering algorithms, SQL, and deep model.
- Experience in C++, Dremel/F1, and TensorFlow.
- Experience in Research.
- Ability to drive quality projects end-to-end from design to implementation to eventual launch.
About the job
We are the core team responsible for Search Ads Personalization. Our personalization models and infrastructure permeate through every part of Search Ads - from Ad Retrieval, Ranking, Auction and Creative writing. As a result, our problems are challenging both in depth (understanding nuances of user's needs), and breadth (scale across entire Google Search user base). We work on modeling user preferences and tasks using a myriad of technologies embedding models, language models, as well as building real-time and large scale inference infrastructure.
Personalizing our Ads has proven to be extremely successful, and as a result, it is a key objective of entire Ads organization. Join us to be in the centre of this personalization revolution! Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.
The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Focus on personalization (P13N) in the Generative AI space (ads on AIM, personalized ads creatives).
- Collaborate across GenAI teams in Ads Quality and GDM to create and execute on a goal/roadmap to ensure that our users feel connected with our GenAI Ads.
- Work on writing ads creatives or ad explanations to account for user tasks and preferences.
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.
Tips 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.