Staff Research Engineer, Applied AI

About the job

We are seeking a Research Engineer to lead the development and deployment of novel applications, leveraging Google’s generative AI models. This role focuses on rapidly developing new features, and working across partner teams to deliver solutions, and maximize impact for Google and top Google customers. You will be instrumental in translating cutting-edge AI research into real-world products, and demonstrating the capabilities of latest generation models. We are looking for engineers with a strong track record of building and shipping software, ideally with experience in early-stage environments where they have contributed to scaling products from initial concept to production. You will be motivated by the opportunity to drive product and business impact.

This role requires a strong software engineering foundation and a passion for building and iterating on software products, using state-of-the-art GenAI tools. We are looking for engineers who thrive in fast-paced environments and have a proven ability to deliver high-quality code. Experience in prototyping, building, and scaling software is essential. We particularly value experience in early-stage or startup environments, where engineers take ownership and drive product development from the ground up.

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.

Responsibilities

• Partner closely with external parties and internal cross-functional teams to navigate ambiguity, deeply understand real-world challenges, and define clear product objectives and technical designs.
• Drive the curation of specialized datasets, design rigorous evaluations across various industry verticals, and execute model fine-tuning to achieve optimal real-world performance.
• Lead the engineering and development of novel solutions from 0 to 1, utilizing internal platforms and tools to build sophisticated agents and workflows powered by GDM foundation models.
• Synthesize and upstream learnings from third-party partners to the core research teams by sharing real-world evaluations, edge cases, and deployment signals which can inform the development of future frontier models.
• Act as a technical lead in the applied AI space, setting best practices for genAI deployment and demonstrating the peak capabilities of frontier models in solving high-impact problems end-to-end.

Qualifications

Minimum qualifications:

• Bachelor's degree in Computer Science, Electrical Engineering, a related technical field, or equivalent practical experience.
• 8 years of experience in software development with one or more programming languages (e.g., Python), and with data structures or algorithms.
• 5 years of experience in ML design and optimizing ML infrastructure (e.g., model deployment, evaluation, data processing, debugging, and fine-tuning), and experience in media generation or reinforcement learning.
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

Preferred qualifications:

• Experience with generative AI models and applications.
• Experience with front end development.
• Experience in rapidly developing and shipping software products in a fast-paced, customer-facing startup-like environment, demonstrating adaptability to changing priorities.
• Experience with cloud computing platforms and infrastructure (e.g., Google Cloud Platform).
• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face, etc.
• Contributions to open-source projects.