Staff Software Engineer, AI/ML, Data Infrastructure, Workspace

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience with one or more of the following: ML infrastructure or specialization in another ML field.
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
• Experience in distributed and large-scale databases used for ML and AI training and evaluation etc.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of experience with data structures and algorithms.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• 3 years of experience in a technical leadership role leading project teams, setting technical direction, and designing and architecting software solutions.

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.

The Workspace Abuse Safety Protections (WASP) team is responsible for keeping Google Workspace products and users safe from abuse. The Machine Learning for Abuse and Safety Solutions (MASS) team is a horizontal team within WASP. We provide machine learning solutions including components, pipelines, infrastructure, and modeling approaches to combat abuse across all Workspace products, such as Gmail, Google Drive, and GChat, including the GenAI features on these products.

We aim to build and scale reliable and efficient ML systems to protect billions of users.

AI will change the future of work in profound ways, and our products— Gmail, Docs, Drive, Calendar, Sheets, Vids and Meet are at the forefront. From pre-computed summaries for email threads, summaries for meetings, and videos created from a document using lifelike AI avatars, our AI opportunity is huge. Our mission is to meaningfully connect people so they can create, build, and grow together and as part of the team you can build how productivity tools should work 5-10 years into the future. You will work with model builders (Google DeepMind), work with exceptional leaders, and have the ability to impact billions of users across the world.

The US base salary range for this full-time position is $197,000-$291,000 + 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.

Responsibilities

• Define and drive the implementation of technical roadmap and best practices for ML infrastructure within WASP. Build and support infrastructure for large-scale data ingestion and transformation for ML model consumption. Design, build, test, and maintain scalable, reliable, and efficient infrastructure and platforms for the machine learning life-cycle.
• Develop and improve ML pipelines, feature engineering and storage systems, and model management tools. Enhance and integrate with Google's ML platforms and technologies underlying data processing frameworks like Flume. Ensure the performance, reliability, and resource efficiency of ML training and servi