Senior Software Engineer, Generative AI/Machine Learning, Google Ads

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 Customer Experience Channels team within Google Ads is building the core AI-driven platform that connects Google with our global advertisers. Our mission is to meet customers exactly where they are and maximize their business success by leveraging agentic AI, Web UI control, and advanced Large Language Models (LLMs).

Rather than relying on static help centers, we run a multi-turn agentic framework that dynamically manages customer support inquiries. The system determines whether to resolve advertiser issues via automated, hyper-personalized responses or seamlessly transition them to human specialists. Our upcoming technology roadmap focuses on a comprehensive AI concierge that acts as a troubleshooting partner, streamlining workflows and scaling operational efficiency worldwide.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

• Write and test product or system development code, focusing on end-to-end features spanning user-facing interfaces (chat, email, and video) down to backend APIs, storage schemas, and databases.
• Collaborate with peers and stakeholders through design and code reviews. Partner with peers via design docs and code reviews to ensure system accuracy, testability, and adherence to engineering best practices.
• Contribute to existing documentation or educational content: Create and update technical documentation, API specifications, and developer guides based on product updates and user feedback.
• Triage, debug, and resolve platform issues, analyzing root causes and their downstream impacts on system and service operations.
• Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field, focusing on agentic prompts and multi-turn conversational workflows.

Minimum qualifications:

• Bachelor’s degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.
• 5 years of experience with software development in one or more programming languages (e.g., Java, C++, Python, or Go).
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
  
• 3 years of experience with one or more of the following: Speech/audio, reinforcement learning, ML infrastructure or specialization in another ML field.
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging, prompt engineering, LLM output validation).

Preferred qualifications:

• Master's or PhD in Computer Science, Mathematics, or related technical field.
• 5 years of experience with data structures or algorithms (including optimizing graph traversal, search efficiency, or data processing pipelines in high-throughput applications).
• 1 year of experience in a technical leadership role, manging end-to-end project delivery, conducting architectural reviews, or mentoring engineering peers.
• Experience developing accessible technologies to ensure our user-facing communication interfaces are accessible to all business users.
• Experience with full-stack development spanning backend systems and frontend frameworks (such as Dart, JavaScript, TypeScript), and direct experience with shadow-mode testing or batch evaluation workflows.