Software Engineer, Content Safety

• 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.

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).
• 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.
• Demonstrated accountability for managing technical debt, reducing bug counts, and mitigating SLO breaches to maintain high operational standards.

• 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).