Staff Software Engineer, Machine Learning, Secure Storage Infrastructure

Key Skills

C++, Database, Design, Machine learning techniques, SQL

Job Description

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development, and with data structures/algorithms.
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
• Experience with server-side programming, C++, database design, and data encryption.

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, and/or cross-business projects.
• Experience with ML infrastructure, privacy data handling, security analysis, open-source contributions, or coding in Java or Rust.

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.

In this role, you will be working closely with other research and product teams to support a personalized LLM agent. You will be responsible for building a secure storage infrastructure where the user’s personal and private data will be kept, and is used for private training and inference for LLM agents.

Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.

The US base salary range for this full-time position is $185,000-$283,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. 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.

Responsibilities

• Facilitate alignment and clarity across teams on goals, outcomes, and timelines.
• Design, develop, test, deploy, maintain, and enhance large-scale software solutions.
• Explore, experiment, and evaluate the latest internal and open-source community efforts (e.g. frameworks/models) in the generative AI space.
• Collaborate effectively across teams in research and other product areas and work with both internal and open-source systems.
• Propose encrypted storage architectures and solutions for ML applications (e.g. storage and fast retrieval in vector databases).