AI Data Engineer Jobs at Google with Visa Sponsorship
AI Data Engineer jobs at Google sit at the intersection of large-scale data infrastructure and machine learning systems, covering pipelines, feature engineering, and model deployment at significant scale. Google has a consistent track record of sponsoring work visas for this function, including H-1B visa, H-1B1 visa, and E-3 visa classifications.
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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 AI Data Engineer Jobs at Google
Align your portfolio to Google's stack
Google's AI Data Engineer roles prioritize hands-on experience with distributed systems, BigQuery, Vertex AI, and TensorFlow. Structure your portfolio around end-to-end ML pipelines and large-scale data processing to match what their engineering teams are actively building.
Target roles listed under specific product teams
Google posts AI Data Engineer positions across Google Cloud, DeepMind, and Google Research. Applying to team-specific openings rather than generic postings signals a clearer fit and gets your application to hiring managers closer to the actual work.
Confirm your visa type before the offer stage
Google sponsors H-1B, H-1B1 visa, and E-3 visas. Knowing which classification applies to your nationality before final-round interviews helps you respond clearly when compensation and start-date conversations begin, avoiding delays in the offer process.
Prepare for H-1B cap timing if you're cap-subject
If you need a cap-subject H-1B, USCIS registration opens in March for an October 1 start date. Receiving an offer in Q4 or Q1 means you'll likely wait through a full lottery cycle, so negotiate start dates and interim work authorization options with your recruiter early.
Use Migrate Mate to filter verified AI Data Engineer openings
Identifying which Google roles are actively open to visa sponsorship takes real research. Use Migrate Mate to filter AI Data Engineer jobs at Google by visa type, so you're applying to roles where sponsorship is confirmed rather than assumed.
Request clarity on LCA filing and prevailing wage tier
Google files a Labor Condition Application with the DOL before your H-1B petition can proceed. Ask your recruiter which wage level the role is certified at, since Level I and Level II certifications can affect the offer structure and your petition's approvability.
Frequently Asked Questions
Does Google sponsor H-1B visas for AI Data Engineers?
Yes, Google sponsors H-1B visas for AI Data Engineer roles. They also sponsor H-1B1 visas for Chilean and Singaporean nationals and E-3 visas for Australian nationals. The specific classification depends on your citizenship. Google's legal and HR teams manage the petition process internally, so your recruiter will coordinate the filing once you have a confirmed offer.
Which visa types does Google commonly use for AI Data Engineer roles?
Google uses the H-1B for most international AI Data Engineers, the H-1B1 visa for Chilean and Singaporean nationals, and the E-3 exclusively for Australian citizens. AI Data Engineer roles qualify as specialty occupations under USCIS guidelines because they require at minimum a bachelor's degree in computer science, data engineering, or a closely related field.
How do I apply for AI Data Engineer jobs at Google?
Applications go through Google's careers portal at careers.google.com. Search for AI Data Engineer roles filtered by location and team. The process typically involves an initial recruiter screen, followed by technical phone interviews covering data systems and ML infrastructure, then a virtual on-site loop. You can also browse open, visa-sponsorship-confirmed AI Data Engineer positions at Google through Migrate Mate before applying directly.
What qualifications does Google expect for AI Data Engineer roles?
Google expects a bachelor's or master's degree in computer science, data engineering, or a related technical field. Beyond credentials, strong candidates demonstrate production-level experience with distributed data pipelines, proficiency in Python or SQL, and familiarity with ML workflow tooling. For more senior levels, prior work on feature stores, model serving infrastructure, or real-time data systems is a practical differentiator.
How do I plan my timeline if I need Google to sponsor my visa?
Timeline depends on your visa type. E-3 and H-1B1 visas have no annual cap and can be filed throughout the year, so start dates are more flexible. Cap-subject H-1B petitions require USCIS registration in March, with employment beginning no earlier than October 1. If you're cap-subject, factor in a six-to-twelve month gap between offer and start date, and confirm whether Google offers cap-gap or OPT extension support during that window.