Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software programming in Python, Java, or C++.
• 1 year of experience in a core ML domain, such as generative AI, Natural Language Processing (NLP), computer vision, speech/audio, reinforcement learning, recommendation systems, or ML infrastructure.
• 1 year of experience with ML infrastructure (e.g., model training, model inference, model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:

• Experience in safety-adjacent domains, including factuality, product policy, or broader responsible AI frameworks.
• Experience managing safety for UGC or GenAI products, with a deep understanding of adversarial incentives, abuse vectors, and distribution dynamics like virality.
• Experience designing and deploying global-scale defensive architectures and pipelines capable of meeting rigorous Service Level Objectives (SLOs).
• Demonstrated accountability for managing technical debt, reducing bug counts, and mitigating SLO breaches to maintain high operational standards.
• Solid high-level understanding of Machine Learning and Large Language Model (LLM) architecture, specifically transformers, activations, and the requirements for efficient, large-scale training and deployment.

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 mission in the Content Safety organization is to protect Google’s users and the internet as a whole from exposure to offensive, sensitive or potentially harmful content. We achieve this by contributing directly to our foundational models and collaborating closely with DeepMind.

Beyond keeping users safe at scale, we also play a key role in accelerating Google's product launches by providing product teams with the right tools to explore new ideas and products following our Responsible AI principles. Our team combines unique subject matter expertise in the content safety domain, ML and high-throughput infrastructure.

In this role, you will keep society safer. You will work on problems like transformer architecture, and find comfort in an ever-changing landscape, anticipating threats that don't exist yet.

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

• Design, build, and scale content safety systems including classifiers, vector databases, and multimodal models to protect business-critical products and GenAI experiences.
• Develop and maintain production-grade distributed systems and content processing pipelines optimized for high throughput and reliability across server-side and on-device environments.
• Model training, evaluation, and productionization workflows, incorporating feedback loops and automation to continuously improve model quality and performance.
• Implement agentic workflows and advanced heuristics for deep threat understanding, enabling the proactive detection of complex abuse patterns.
• Drive agile engineering efforts to identify and mitigate novel abuse patterns, ensuring Google’s products remain engaged and safe in a shifting threat landscape.