Senior Staff Software Engineer, Generative AI, Agentic AI

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
  
• 7 years of experience leading technical project strategy, machine learning (ML) design, and optimizing industry ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience with design and architecture; and testing/launching software products.
  
• 2 years of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of experience with data structures and algorithms.
• 8 years of experience in ML/AI focused software development with a quality focused approach.
• 6 years of experience in ML design, quality hillclimbing, optimizing and scaling the ML models (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  
• 5 years of experience in a technical leadership role leading project teams and setting technical direction.
• 3 years of experience working in a matrixed organization involving cross-functional, or cross-business projects.

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.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

In this role, you will build enterprise products WITH AI AGENTS that move beyond thinking to doing owning and automating entire workflows to shift enterprise work from human-centric processes to agentic, outcome-driven execution.

Applied AI builds conversational agents deployed at a large scale that achieve very meaningful results in the real world. Some examples include the customer agent built for large call center environments, to fast food ordering handled by our Food AI agent. The team is transforming how enterprises connect with customers through the power of AI. We also offer unique experiences for team members where you get to work directly with the model builders (Google DeepMind / Vertex), learn and work with brilliant AI leaders, and have access to Global 1000 customers via our existing Google Cloud relationships. The opportunity in this space is tremendous.

The US base salary range for this full-time position is $248,000-$349,000 + 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

• Lead the strategy, design and implementation of the agentic AI systems for enterprise use cases.
• Build the enterprise AI agents, with a quality focussed approach (e.g., prioritizing eval, hillclimbing, scalability perspective).
  
• Develop quality centric approaches for multi-agent orchestration and architecture, enabling specialized agents to work together seamlessly to achieve complex goals together.
  
• Use foundational capabilities for agent tool use and reasoning, allowing agents to autonomously interact with APIs, databases, and other applications to accomplish their tasks.
  
• Build systems for evaluation, loss-pattern analysis to ensure systematic quality hill climbing for agents and collaborate with key partn