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 Experience/Conversational Experience as a Service (CXAS) Console team is harnessing the power of Google's next generation agentic development platform to empower anyone to build enterprise grade agents. We work across the development lifecycle - Build, Evaluate, Deploy, Monitor and Optimize, extending the console, and re-imagining workflows in an agentic era.
Our work is an intersection of AI centric workflows, in an evolving AI infused front-end serving customers around the world at scale.
In this role, you will experience light bulb moments everyday, in understanding how AI can be used reliably in real world scenarios, with high profile customers, both in creating agents, and running them. You will be working in a smart, collaborative and goal oriented team.
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 unprecedented speed. Join us to work on cutting-edge projects and shape the future of AI in a fast-paced, collaborative, and impactful environment.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Canada: $182000 - $186000 (CAD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Design and scale secure infrastructure on Google Cloud Platform (GCP) that powers the entire generative AI agent lifecycle, empowering developers to seamlessly build, evaluate, deploy, monitor, and optimize agents.
- Bring strong system sensibility to design and implement high-performance APIs and backend services that enable developers to build AI agents capable of complex, open-ended dialogues and autonomous task completion.
- Maintain the complex data processing pipelines and backend logic required to reliably support AI-centric workflows, ensuring the platform serves enterprise customers around the world at scale.
- Advocate back-end security and reliability best practices, building systems capable of safely supporting AI agents interacting in complex, real-world scenarios with high-profile customers.
- Lead the back-end technical goal, solve ambiguous technical issues, and mentor developing teams to rapidly deliver new 0-to-1 capabilities with startup agility while directly collaborating with model builders.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 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 (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), 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).
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical field.
- 5 years of experience with data structures and algorithms.
- 1 year of experience in a technical leadership role.
- Experience developing accessible technologies.