Software Engineer, Foundational ML Research

Minimum qualifications: Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience. 5 years of experience programming in Python. Experience with training machine learning models. Preferred qualifications: PhD in Computer Science or equivalent field. 5 years of experience with data structures/algorithms. Experience publishing in venues (e.g., NeurIPS, ICML, COLT, ICLR) or contributing to open-source projects related to ML. Experience with ML frameworks (JAX, PyTorch) for large-scale model training. Experience applying ML research to generative models, specifically in areas of post-training Gemini models. About the job 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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Prototype and test fundamental algorithmic approaches. Write and test product or system development code. Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency,) and bring solutions into production environments Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback. Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality. Design and implement solutions in one or more specialized ML areas such as reinforcement learning, optimization, privacy, and game theory.