Staff Software Engineer, Applied AI Engineering

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 2 years of experience with 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.
• Experience building advanced GenAI (multi-step LLM, multi-agent systems, or model context protocol (MCP) integrated with orchestration frameworks).
• Working knowledge of leverage AI/ML, automation, and advanced technologies to optimize workflows and enhance efficiency.
• Demonstrated ability to lead and deliver on complex, ambiguous projects. Excellent problem-solving, investigative, and technical leadership skills.

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.

Google AI Science on Cloud (a collaboration across DeepMind, Research, and Cloud) accelerates scientific breakthroughs, establishing Google as the premier platform for complex industry problems.

As a Staff Software Engineer in the Research and Incubation team, you will be a principal architect bridging AI research and enterprise production. You will define technical roadmaps, establish architectural standards, and engineer zero-to-one AI platforms. By managing multi-agent orchestration, reasoning pipelines, and multimodal models, you will translate theoretical Machine learning (ML) breakthroughs into highly optimized, scalable services, acting as the key technical bridge between DeepMind, Research, Product Management (PM), and enterprise partners.

The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Architect secure, highly cost-effective distributed systems and MLOps pipelines on Google Cloud Platform (GCP) for fine-tuning and serving frontier models.
• Translate AI/ML research into production-grade services, optimizing model inference latency, throughput, and large-scale TPU/GPU fleet utilization.
• Design robust, self-improving multi-agent architectures and cognitive planning engines capable of executing complex, guardrailed workflows.
• Shape Google Cloud’s AI roadmap by feeding operational insights and technical gaps from incubation projects directly back to core engineering.
• Mentor executive engineers across teams, driving a culture of rapid prototyping, rigorous system design, and technical excellence.