Software Engineer III, AI/ML, AI Data

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

As a Software Engineer (Research), you will help invent, prototype, and build next-generation techniques and pipelines for analyzing model behavior and evaluating complex reasoning. Set at the intersection of applied research and software engineering, you would involve in exploring new methods to assess model failure modes, prototyping novel evaluation tasks, and turning experimental insights into robust, scalable benchmarks and data flywheels. Working alongside researchers and engineers across Google DeepMind and AI Data, you would run empirical analyses, build programmatic verifiers, and help unlock better ways to measure frontier model capabilities across code, math, and specialized domains.

Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

• Research, experiment and prototype novel approaches for analyzing model behavior and failure patterns.
• Conduct targeted empirical investigations into model outputs, agent trajectories, and error patterns to inform benchmark design and improve evaluation trustworthiness.
• Implement, test, and refine benchmark suites evaluating complex agentic reasoning across code, math, and specialized domains, ensuring high discriminability and zero data contamination.
• Develop and maintain pipelines that transform benchmark/sandbox execution logs, agent traces, and runtime artifacts into high-fidelity evaluation datasets and training data.
• Partner closely with research scientists, senior engineers, and cross-functional teams to translate research insights into practical evaluation infrastructure.