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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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.
With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
As a part of the Discover Personalization team, you will help people feel positively connected and informed about the world around them by delivering the pulse of the Internet that matters to you, within Google Search. You will contribute to the key product's appeal, which lies in having an understanding of users and will be laser focused on building foundational user models for users using all of their interactions across Google products, and leveraging them to power Discover’s retrieval and ranking. You will manage some of the toughest ML and Quality problems, including Neural Deep Retrieval, Activity Clustering, Reinforcement Learning and Multi-Objective Ranking.
In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
ROLE AND RESPONSIBILITIES
- Design and implement personalized user models to optimize for user happiness, including Neural Deep Retrieval Models, Deep Neural Network Ranking/Scoring models, User/Content Clustering Models, Large Language Models (LLM)-based Retrieval Augmented Generation Models, and more.
- Build user and content clustering models to enable core personalization and ranking use cases.
- Enhance model performance and personalization precision/recall through advanced modeling techniques such as transformers, distillation, reward shaping, multi-task learning, neural bandits, etc. and capabilities through feature engineering, automatic parameter tuning, label quality engineering, etc.
- Scale the model's applications to a multitude of modalities (content, queries, videos and notifications) and use cases (retrieval, ranking, content generation, diversification, etc.).
- Create next-generation realtime ML models that can capture new user interests and world trends in seconds and scale model training and serving to billions of users.
BASIC QUALIFICATIONS
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 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 ML or Quality experience working on recommendation systems.
- Experience in recommender systems, clustering algorithms, SQL, deep model.
- Experience in C++, Dremel/F1 and TensorFlow.
- Experience working with research.
- Ability to drive quality projects end-to-end from design to implementation to eventual launch.
COMPENSATION
- Salary Range: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
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.
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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.