Staff Software Engineer, AI Data Multimodal

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 with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience with full stack development, across back-end such as Java, Python, Golang, or C++ codebases, and front-end.

Preferred Qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of experience with data structures/algorithms.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience with innovation of technology at scale and passion for development and the use of cross-platform shared code.
• Understanding of ML Systems and Infrastructure for production with technical knowledge to be credible with customers and engineers.
• Understanding of genAI model development workflows for post-training and product fine-tuning, especially multimodal and generative media.

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.

In this role, you will build systems to secure such high-quality data and also improve velocity for ML researchers and product developers to use the data efficiently for model training/fine-tuning and product adoption.

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 $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

• Responsible for technical leadership and IC work in building and enhancing Google wide infrastructure for multimodal and GenMedia use-cases.
• Design and build scalable, general-purpose multimodal data tooling and infrastructure to support a wide range of research and product areas for multimodal understanding (Gemini) and multimodal generation (Nano Banana, Veo).
• Take on challenges in multimodal data such as data sourcing, data sampling, evaluation automation, loss analysis.
• Build and optimize infrastructure for developing and deploying multimodal signals and autoraters, including tools for prompting, large-scale inference, visualization, and evaluation.
• Address unique and emerging technical challenges of the rapidly evolving field of multimodal AI, working to create stable and impactful solutions for a dynamic landscape.