Software Engineer

Google•Mountain View, CA

13h•$149,400 - $211,000•Hybrid

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics or a related field and 2 years of experience in the job offered or in a Software Engineer-related occupation.
Software development using Java, C, C++, Python, or Go;
Designing and applying data structures or algorithms;
Systems thinking or analyzing technical problems from a broad, systems-level perspective;
Managing the full lifecycle of applied research or machine learning projects from proof-of-concept to implementation;
Data analysis and synthesis to generate solutions or evaluate outcomes for machine learning applications.

Responsibilities

Apply research to high-impact problems by prototyping GenAI solutions, curating datasets, and building ML pipelines for generative media, multimodal understanding, and reinforcement learning;
Develop and test robust product code, performing comprehensive testing that includes integration, performance, and security to ensure system quality and reliability. Design and build infrastructure to support next-gen AI features;
Collaborate with peers through rigorous design and code reviews to enforce best practices, improve system testability, and ensure overall efficiency and accuracy;
Triage and resolve complex system issues by debugging, analyzing root causes, and implementing solutions to optimize hardware, network, and service operations;
Create and maintain technical documentation and educational materials, adapting content based on product updates and user feedback to ensure clarity and relevance; and manage the full deployment lifecycle by contributing to system qualification, monitoring, process automation, and paying down technical debt to improve long-term scalability.