Staff AI Research Scientist, Applied AI, Google Cloud

Staff AI Research Scientist Applied AI Google Cloud - Google Careers

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Staff AI Research Scientist Applied AI Google Cloud

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Location: Sunnyvale, CA, USA

Advanced Experience owning outcomes and decision making, solving ambiguous problems, and influencing stakeholders. Deep expertise in domain.

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Minimum qualifications

• PhD degree in Computer Science, a related field, or equivalent practical experience.
• 4 years of experience with research agendas across multiple teams or projects.
• 2 years of experience with machine learning algorithms and tools or Applied ML (e.g., LLMs, Generative AI, NLP, Recommendations).
• Experience coding in Python, JavaScript, R, Java, or C.
• One or more scientific publication submissions for conferences, journals, or public repositories (e.g., CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).

Preferred qualifications

• 2 years of experience in coding and leading multiple research efforts and influencing research direction.
• Experience developing and deploying AI solutions to solve large-scale problems.
• Experience with the entire product development process from initial ideation and research to prototyping, testing, and final implementation.
• Ability to articulate technical concepts and influence technical directions.
• Excellent communication skills.

About the job As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution, and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, youll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine and deep learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, youll also actively contribute to the wider research community by sharing and publishing your findings with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

The Cloud Applied AI teams goal is to support enterprises to innovate conversational AI products such as contact centers, conversational interfaces on websites, devices, and mobile apps. We apply the art of Artificial Intelligence (AI) and Machine Learning (ML) to the conversational problems and work on building the next generation of customer service.

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

• Design, develop, and deploy scalable and agentic AI solutions for enterprise use cases across domains like finance, sales, marketing, and retail, focusing on innovation and utility with a user-centric perspective.
• Take ownership of AI quality for production systems by defining technical metrics aligned with business goals, implementing evaluation frameworks, designing experiments, analyzing loss patterns, and driving improvements through system changes or training data enhancements.
• Implement, optimize, and advance AI techniques with both training-free and training-based approaches.
• Drive progress through experimentation cycles such as proposing hypotheses, designing validation methods, implementing and testing ideas, analyzing results, and iterating quickly to find optimal solutions.
• Provide technical leadership on projects and facilitate clarity on goals and timelines. Work with a small, experienced team of engineers and researchers, engaging in design and code reviews to foster a culture of continuous improvement.