Software Engineer III, Generative AI, Agent Engineering

Google•Kirkland, WA

12d•$147,000 - $211,000

About The Position

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 an Applied AI Agent Engineer (AE), you will be at the forefront of applying Google's Generative AI to solve real-world challenges for our most strategic customers. You will act as a specialized software engineer embedded with key accounts, working to build, deploy, and optimize sophisticated AI agents on our CES platform. This is a high-impact role that bridges the gap between our core product engineering teams and complex customer needs, directly accelerating customer success and shaping the future of our AI products. The Cloud Applied AI (AAI) powers business growth with Gemini Enterprise. Our portfolio includes Gemini Enterprise for Customer Experience (Shopping Agent, CX Agent Studio, Agent Assist, Vertex AI Search - Commerce, Customer Experience Insights), along with other vertical and domain packaged solutions. We enable high adoption and speed to value by building solutions that are quickly deployed, delivering new 0-to-1 capabilities with startup agility. Team members operate at the forefront of AI, collaborating directly with model builders with unprecented speed. Join us to work on cutting-edge projects and shape the future of AI in a fast-paced, collaborative, and impactful environment. The US base salary range for this full-time position is $147,000-$211,000 + 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 [https://careers.google.com/benefits/].

Requirements

Bachelor’s degree or equivalent practical experience.
2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).

Nice To Haves

Master's degree or PhD in Computer Science or related technical fields.
Experience in prompt development, model evaluation, and the creative application of Artificial Intelligence (AI).
Experience in Vertex AI, BigQuery, Cloud Storage, Dialogflow.

Responsibilities

Partner directly with select, strategic customers to understand their business challenges. Design, co-develop, debug, and deploy custom conversational AI agents and solutions to accelerate their time to value.
Act as a high-level problem solver, empowered to write bespoke code, develop custom tooling, and even contribute directly to the core product codebase to resolve critical customer issues.
Systematize your learnings from customer engagements by creating reusable tools, building robust documentation and accelerators, and establishing best practices that can be used across the organization.
Serve as a critical feedback loop to our core Product and Engineering teams. Synthesize firsthand insights from the field to influence product strategy, identify feature gaps.
Guide customers on agent performance, CI/CD, and production readiness.