Head of Research, Professional Intelligence

About the job

Working alongside our Advanced Agentic team, the Specialized Professional Intelligence team is building domain-specific AI architecture for high-stakes industries that operate on vast, sensitive, and private

datasets. The team also contributes to the broader Gemini model training effort by building evaluations, datasets and training objectives that measure performance on complex tasks. Additionally, we work on research to see how we can improve agent performance throughout these product surfaces. We are looking for roles across the board for strong infrastructure engineers, front-end engineers, and researchers excited to achieve unprecedented levels of performance.

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.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Act like an owner; be fearless in diving deep, asking questions, proposing solutions, establishing consensus and then making things happen.
• Lead, mentor, and grow a team of research scientists and machine learning engineers.
• Provide direction and focus in areas of high ambiguity while prioritizing team workloads and managing projects to enable rapid iteration on research initiatives.
• Oversee the development of benchmark datasets and rigorous evaluation suites measuring factual accuracy, citation fidelity, and multi-step reasoning.
• Partner with engineering, product management, and legal domain experts to deploy research advances to early enterprise partners.

Qualifications

Minimum qualifications:

• PhD in Computer Science, Mathematics, Applied Statistics, Machine Learning, or a related quantitative field, or equivalent practical experience.
• 10 years of experience in applied NLP research and productionization, including experience with advanced prompting, and LLM fine-tuning or reinforcement learning (RL).
• 4 years of experience leading technical or research teams and mentoring engineers.

Preferred qualifications:

• Startup experience iterating fast and exploring multiple strategies to optimize performance.
• Exposure to production systems that rely on ML models, or experience with model deployment.
• Significant success with building AI software and systems for specific industries, either as research publications at top PL or ML venues, or as open source artifacts.
• Track record of delivering high quality solutions to large, complex software problems.
• Work Independently, committed to safe, transformative AI, and excellent written and verbal communication and strong stakeholder management across all levels.
• Demonstrated ability to deliver clean, maintainable, production-grade code in Python, adhering to sound engineering principles.