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Job Details
Posted date: Feb 20, 2026
Location: Seattle, WA
Level: Executive
Estimated salary: $354,000
Range: $294,000 - $414,000
Description
Identify key opportunities and challenges in the model training, inference, RL and Agentic platforms where Storage can accelerate the development and performance of production systems. Build a strategy to stay ahead of the needs of the next generation systems. Drive the architecture, design, and implementation of highly scalable, performant, reliable, and secure storage system. Derive the best architecture based on first principles thinking and work across the stack. Lead AI innovation by forecasting trends and defining the next-gen platform roadmap. Partner with other teams like Hardware, Data Center, Networking, and other software platforms, and influence alignment around common solutions. Solve critical AI pain points for internal and external customers to maximize platform value.Generative AI is revolutionizing all aspects of technology. Storage is a foundational technology needed for building the next generation models and using them in applications. Storage also represents a huge opportunity to deliver value to enterprises using the rich unstructured data sets available.
As Director, you will drive and develop the next generation Storage infrastructure to act as the foundation of training, inference, and Reinforcement Learning (RL) platforms as they evolve. You will also drive the capabilities in the platform which allow Agents for our customers to maximize the value of the unstructured data that they have stored. You'll collaborate with an excellent team of engineers across Google, driving full stack innovation from data center, hardware, and software, and ensure Google and Google Cloud provide the best AI products in the industry. Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
The US base salary range for this full-time position is $294,000-$414,000 + 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.
Qualifications
Minimum qualifications: Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. 15 years of experience in software engineering, with 8 years in a technical engineering role. Experience building and scaling distributed systems and platforms (e.g., Kubernetes, microservices, Kafka/RabbitMQ, Redis/Cassandra, load balancing, sharding, CAP Theorem, service mesh). Experience in machine learning concepts, AI architectures, and related technologies (e.g.,transformer architectures, LLM fine-tuning (LoRA, QLoA), Retrieval-Augmented Generation, latency optimization, model quantization, hyperparameter tuning, PyTorch/TensorFlow).Preferred qualifications: Master's degree or PhD in Computer Science, Artificial Intelligence, or a related field. Experience with model training, inference, AI infrastructure, Agents and unstructured data platforms. Understanding of industry trends and competitive landscapes in AI and machine learning. Ability to drive innovation and foster a culture of technical excellence. Excellent communication and presentation skills, with the ability to articulate complex technical concepts to unique audiences.
Extended Qualifications
Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. 15 years of experience in software engineering, with 8 years in a technical engineering role. Experience building and scaling distributed systems and platforms (e.g., Kubernetes, microservices, Kafka/RabbitMQ, Redis/Cassandra, load balancing, sharding, CAP Theorem, service mesh). Experience in machine learning concepts, AI architectures, and related technologies (e.g.,transformer architectures, LLM fine-tuning (LoRA, QLoA), Retrieval-Augmented Generation, latency optimization, model quantization, hyperparameter tuning, PyTorch/TensorFlow).Check out other jobs at Google.