Minimum qualifications:

• Bachelor’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics or a related field and 4 years of 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 2 years of experience in the job offered or in a Software Engineer-related occupation.
• Position requires 2 years of experience in the following: Development of machine learning models for speech and audio domains or application of reinforcement learning for sequential decision making; Data pipeline development for managing and processing large-scale user data; Software design and architecture for complex systems; Python or C++ for machine learning applications; Leading ML design for organization-scale projects

About the job

The US base salary range for this full-time position is $207,000 - $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 New York, NY 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

• Design, develop, test, and deploy large-scale software solutions for a state-of-the-art personalized AI assistant
• 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.