Google is looking to develop its next-generation intelligent storage layer, a foundational system designed to unlock the semantic meaning of unstructured data, handling trillions of objects and exabytes of data.
Requirements
- 8 years of experience in software development.
- 7 years of experience with distributed systems and building large-scale data platforms.
- 7 years of experience leading technical project strategy, ML design, and working with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience in embedding generation techniques and models (e.g., text, image, and audio).
- Experience in designing and implementing vector search and similarity search systems at scale (e.g., using technologies like ScaNN, Faiss, or HNSW).
- Experience with cloud storage services and distributed file systems.
Responsibilities
- Architect and build the systems for a smart storage platform on top of GCS. This includes scalable pipelines for embedding generation, efficient vector search indexes, and the related platform infrastructure.
- Innovate and define new storage APIs and data models that expose semantic understanding to customers.
- Solve complex scaling challenges related to handling a massive volume of unstructured data and high-QPS workloads for real-time AI applications.
- Collaborate with machine learning experts and researchers to integrate models for data embedding and understanding.
- Lead technical design reviews, mentor junior engineers, and drive the overall technical direction for the team.
Other
- Bachelor’s degree or equivalent practical experience.
- Master’s degree or PhD in Engineering, Computer Science, Machine Learning or a related technical field (preferred).
- 5 years of experience in a technical leadership role leading project teams and setting technical direction (preferred).
- Ability to work in a fast-paced environment and adapt to changing priorities.
