Senior Software Engineer, Generative AI, Core

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience training and deploying generative models, with a focus on real-world applications
• Experience working with large datasets, data cleaning, pre-processing, and analysis.

Preferred qualifications:

• Master's degree or PhD in Computer Science or a related technical field.
• 5 years of experience with data structures/algorithms.
• Experience with contributing to open-source projects or publications in relevant conferences.
• Experience with doing research work (e.g., graduate work or in prior projects) and working with Gemini models or machine learning frameworks.
• Understanding of deep learning architectures and related algorithms (e.g., Transformers) and deploying machine learning models on Alphabet infrastructure (especially TPUs).
• Excellent communication, collaboration, and problem-solving skills with a passion for innovation and generative 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.

The Developer AI team is transforming the way software development is carried out at Google. We are bringing new capabilities based on the latest Generative AI (GenAI) research to Google developers, and leading the tech industry’s evolution of how software is built.

The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.

The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities:

• Collaborate with DeepMind researchers to train generative models using a unique dataset.
• Partner with Alphabet's internal engineering teams to integrate these models into their workflows, transforming them into a live lab for testing research ideas and rapidly iterating on new approaches.
• Curate and refine software engineering pre-training, instruction tuning, and eval datasets.
• Analyze model outputs and user feedback to continuously improve model performance and enable the use of internal software engineering data for training Gemini models.
• Explore and apply Large Language Models (LLM) post-training techniques to improve model quality for code generation, code transformation and agentic Computational Linguistics (CL) generation and bug-fixing workflows.