Senior Software Engineer, Google Cloud, Applied AI
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages (e.g. C++).
- Experience with Artificial Intelligence, Distributed Systems, LLMs and High Performance Computing.
Preferred qualifications:
- Master’s degree or PhD in Computer Science, or a related field with a focus on Systems or AI.
- Experience building and maintaining multi-agent systems or complex applications in an enterprise setting.
- Experience launching and maintaining high-availability consumer-facing AI products.
- Ability to navigate ambiguity and iterate quickly on customer feedback to deliver solutions that precisely meet market needs.
- Ability to work in a fast-paced, 0-to-1 environment with evolving requirements.
About the job
Google Cloud’s mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren’t just building technology; you’re shaping the frontier of enterprise and driving the evolution of advanced models.
We are redefining the B2B2C software landscape through turnkey agentic products. These products enable businesses to launch engaging and effective experiences across their entire user funnel—from the initial contact to long-term customer loyalty. Anchored by Gemini Enterprise for Customer Experience, our products cater to a variety of horizontal and vertical consumer experiences.
As a Senior Software Engineer, you will handle complex challenges and deliver innovative solutions at breakneck speed. We develop agentic AI solutions for impactful enterprise use cases. You will create AI for real-world, large-scale problems. You will be inventing, designing, and deploying robust systems, focusing on algorithmic innovations for sophisticated agentic AI. This includes improving performance on high-priority directions like multi-agent systems, reinforcement learning, multimodal reasoning, data-centric approaches, evaluation innovations and enhancing Large Language Model (LLM) reliability and tool use for enterprises. Your work will directly influence the next wave of AI innovation, turning research into practical, high-impact solutions that solve critical real-world challenges.
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.US: $174000 - $253000 (USD) + 15% bonus target + bonus + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Design, develop, and deploy scalable and robust agentic AI solutions for high-value, real-world enterprise use cases across domains like finance, sales, marketing, and retail, focusing on innovation and utility, with a user-centric perspective.
- Drive progress through rapid experimentation cycles. This includes proposing hypotheses, designing validation methods, implementing and testing ideas, analyzing results, and iterating quickly to find optimal solutions.
- Contribute to significant advancements in key research areas such as reinforcement learning, multi-modal learning, benchmarking and evals, search/retrieval, adaptation methods for agentic systems, and improving the reliability and tool-use capabilities of LLMs for enterprise-critical tasks to build.
- Handle ambiguous problems with creative, AI-driven solutions.
- Debug, track, and resolve issues efficiently to ensure the high reliability and performance of the AI systems.
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