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.
The Content Safety organization in Core provides safety solutions that protect Google’s users from exposure to offensive, sensitive or harmful content. The team unblock and accelerate Google's product launches by providing quality implementations of company-wide standards for Responsible Artificial Intelligence (AI). The team combines subject matter expertise in the content safety domain and a practice of machine learning and AI-based techniques with the production-grade experience in serving infrastructure and product integrations to protect users from harm, maintain Google’s engagement in the area of online safety across the industry, and make foundational models and the internet safer for society.The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
Responsibilities
- Architect and drive the technical direction for high-quality, future-proof, and performant content safety solutions across both server-side and on-device (e.g., edge) environments.
- Design robust, scalable software architectures, modular interfaces, and complex data models to resolve highly ambiguous and shifting system requirements.
- Lead the development, deployment, and optimization of highly performant agentic flows (e.g., both stateless and stateful) and multi-step AI orchestration pipelines at scale.
- Establish rigorous validation standards, routing logic, and tool-use schemas to ensure deterministic, safe, and compliant execution across autonomous safety agent pipelines.
- Collaborate with global stakeholders and cross-functional partners across regions to translate high-level business goals into concrete technical execution plans.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- 3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 3 years of experience with Machine Learning (ML) infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:
- Experience designing robust, scalable software architectures, modular interfaces, and complex data models to meet ambiguous system requirements.
- Experience in Responsible AI or safety-adjacent fields such as factuality, policy enforcement, or adversarial defense.
- Demonstrated experience designing, scaling, and optimizing complex agentic flows (e.g., stateless and stateful), including advanced prompt engineering, context engineering, and tool-use (function calling) schemas.
- Hands-on experience deploying and scaling GeneAI models, foundation model SDKs, and calling cloud-hosted models in high-throughput environments.
- Solid understanding of ML concepts, Large Language Model (LLM) mechanics (e.g., transformers, activations, embeddings), and how to efficiently train and deploy them at scale.