Software Engineer

Google DeepMind

HybridPosted July 28, 2026

About the role

Google DeepMind is seeking a Software Engineer to join our Mountain View, California team in a hybrid role. The position reports to the Google Mountain View office and may allow a hybrid schedule in line with Google policy.

The US base salary range for this full-time position is $149,400 - $211,000 plus 15% bonus target, equity, and benefits, determined by role, level, and location.

Software Engineer

Google DeepMind

HybridPosted July 28, 2026

About the role

Google DeepMind is seeking a Software Engineer to join our Mountain View, California team in a hybrid role. The position reports to the Google Mountain View office and may allow a hybrid schedule in line with Google policy.

The US base salary range for this full-time position is $149,400 - $211,000 plus 15% bonus target, equity, and benefits, determined by role, level, and location.

Minimum Qualifications: - Bachelor's degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field, and 2 years of experience in software engineering or a software engineer-related occupation. - 2 years of experience in iOS development (Swift/Objective-C), including front-end feature implementation and collaboration with UX/Product Management; designing and applying data structures or algorithms; systems thinking or analyzing technical problems from a broad, systems-level perspective; designing and executing live experiments and evaluating performance data; and managing the full lifecycle of applied research projects from proof-of-concept to implementation.

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 including integration, performance, and security to ensure system quality and reliability.
Collaborate with peers through rigorous design and code reviews to enforce best practices, improve system testability

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