Minimum qualifications:

• Bachelor’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics or a related field and 8 years of progressive post-baccalaureate, experience in the job offered or in a Software Engineer-related occupation.
• Alternatively, will accept a Master’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics or a related field, and 6 years of experience in the job offered or in a Software Engineer-related occupation.
• Position requires 6 years of experience in the following: Software product lifecycle management from testing to launch; Python or C++ for machine learning applications; Algorithm and data structure design to improve system scale and efficiency; Software design and architecture for complex systems; Data pipeline development for managing and processing large-scale user data

About the job

The US base salary range for this full-time position is $264,550 - $300,000 + 20% bonus target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google

Position reports to the Google Mountain View, California office & may allow for a hybrid schedule as per Google policy.

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.

Responsibilities

• Build tools and promulgate best practices for rapid research iteration for researchers building cutting-edge AI systems
• Architect scalable data pipelines and user models for high-fidelity training, simulation, and performance evaluation
• Develop robust evaluation benchmarks, autoraters, and adversarial tests to measure and improve personalization quality
• Collaborate with research scientists and engineers to ship product features and provide technical leadership on high-impact projects
• Review code to ensure best practices and mentor team members on system design and engineering excellence. Define the technical vision and system architecture for key components, ensuring long-term scalability and maintainability.