Staff Software Engineer, Core ML Frameworks

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 testing, and launching software products.
• 3 years of experience with software design and architecture.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, a technical related field, or equivalent practical experience.
• 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 taking complex software projects from initial design and research through to production deployment and maintenance in a large-scale environment.
• Experience building foundational platforms or APIs that serve as a critical dependency for a large set of engineering teams.

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.

Our team is part of the Core Machine Learning organization and focused on accelerating AI innovation across Google. This involves building the premier software stack to support the entire life-cycle for a vast array of machine learning models from GenAI and LLMs to classic deep learning and large recommender systems. By solving the most critical tests of scale and efficiency, we create a single, friction-less path from research to production that empowers Google's products, differentiates Google Cloud, and fosters a global community of innovators.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

The US base salary range for this full-time position is $ + 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.

Responsibilities

• Partner with Google’s various product teams to understand their most complex machine learning tests, spanning the entire Machine Learning (ML) life-cycle from data strategy and modeling to production deployment.
• Design and build solutions that help teams like Search, Ads, YouTube, and Waymo transition to modern AI, guiding critical new models from initial pilot to full production.
• Collaborate closely with leading AI infrastructure teams across Alphabet and Google Cloud to define and build the core frameworks and tools that will power ML development for years to come.
• Build the high-leverage platform that enables Google’s product teams to transform thousands of existing ML pipelines into GenAI-based systems, all while maintaining business continuity and efficiency.