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, you'll 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, you'll 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 (AAI) powers business growth with Gemini Enterprise. Our portfolio includes Gemini Enterprise for Customer Experience (Shopping Agent, CX Agent Studio, Agent Assist, Vertex AI Search - Commerce, Customer Experience Insights), along with other vertical and domain packaged solutions. We enable high adoption and speed to value by building solutions that are quickly deployed, delivering new 0-to-1 capabilities with startup agility. Team members operate at the forefront of AI, collaborating directly with model builders with unprecedented speed. Join us to work on cutting-edge projects and shape the future of AI in a fast-paced, collaborative, and impactful environment.****
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) + 15% bonus target + equity + 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 developers, researchers, engaging in design and code reviews to foster a culture of continuous improvement.
Qualifications
Minimum qualifications:
- PhD in Computer Science, a related field, or equivalent practical experience.
- 2 years of experience leading a research agenda.
- 2 years of experience with machine learning algorithms and tools, or Applied ML (e.g., LLM's, Generative AI, NLP, Recommendations).
- Coding experience in Python, JavaScript, R, Java, or C++.
- One or more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
Preferred qualifications:
- 2 years of coding experience.
- 1 year of experience leading research efforts and influencing other researchers.
- Experience developing and deploying AI solutions to solve large-scale problems.
- Experience implementing genAI tools, AI agents, multi-agent systems, or advanced automation strategies in enterprise environments.
- First-hand knowledge and practical 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.