Staff Software Engineer, AI/ML, AI Data Platform
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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 designing, leading, and scaling large-scale software architectures, distributed systems, or high-throughput data processing platforms.
- Experience working with AI platforms or generative AI technologies (including generative AI agents).
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
- Experience building large-scale distributed systems processing data at scale.
- Experience working in the Enterprise Security space, specifically, building SIEM products.
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.
Google Security Operations is the fastest growing product in GCP and supports customers, partners, and government across the globe. With Google Security Operations, customers are able to discover and stop cyber attacks before they cause harm. With our expertise in global-scale threat intelligence, large-scale cloud computing, big data, and AI, we help our customers and partners understand and solve their most significant security challenges as well as transform and modernize their security operations.
The Data Intelligence team is responsible for processing Exabytes of data at Planetary speed. This includes processing data in real time (streaming), semi-real time (batch processing jobs), and providing data intelligence such as enrichment to enterprise customers.
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Develop an understanding of our schema-on-read pipelines, in-line streaming enrichment, and search indexing architectures. Design, lead, build, and launch critical cloud lakehouse solutions from the ground up, managing safe rollouts across our global customer base.
- Reimagine our data storage tiers, indexing mechanisms, and enrichment capabilities by partnering across engineering and product to enhance core BigQuery and open-table lakehouse services that process exabytes of security telemetry.
- Identify and implement architecture improvements that make our platform simpler, more robust, and horizontally scalable to unleash the full potential of customers’ data for fast SQL analytics, anomaly detection, and AI-driven security workloads.
- Leverage your expertise in SIEM, security data lakes, or large-scale distributed data products to address customer pain points, propose innovative architectures, and deliver high-impact projects while working closely with product and users to incorporate feedback.
- Manage project priorities for yourself and collaborative workstreams. Meet deadlines and deliverables with your technical expertise, proactively identifying risks and communicating schedule adjustments.
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