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.
Find ML Engineer Jobs at GoogleOverview
Showing 5 of 6+ ML Engineer Jobs at Google
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
See all 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 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 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.