Software Engineer II, Content Safety
Summary
Software engineer on Google's Core team in Singapore building content-safety systems — classifiers, vector databases, and multimodal models — to detect abuse and protect Google's products and GenAI experiences. Day-to-day involves training and productionizing ML models and building high-throughput distributed pipelines using Python, Java, or C++.
- 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.
- Train, evaluate, and productionize models, 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.
- 1 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 managing safety for User-Generated Content or GenAI products, with a deep understanding of adversarial incentives, abuse vectors, and distribution dynamics like virality.
- Experience in safety-adjacent domains, including factuality, product policy, or broader Responsible AI frameworks.
- 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.
- Understanding of Machine Learning and Large Language Model (LLM) architecture, specifically transformers, activations, and the requirements for efficient, large-scale training and deployment.