Character.AI
Software Engineer, Backend/Applied ML (Safety & Integrity)
About this role
Character.AI is seeking an experienced Backend/Applied ML engineer to join their Safety Engineering team in designing and scaling systems that address integrity and safety challenges in AI interactions. You'll architect robust backend solutions, develop ML models for content classification and risk detection, and provide technical leadership on platform trustworthiness initiatives.
What you'll do
- Design and maintain scalable backend systems for integrity and safety features
- Lead technical design of solutions to detect and mitigate AI-related risks and content integrity issues
- Develop and deploy machine learning models for content classification, anomaly detection, and risk scoring
- Collaborate cross-functionally with product, data science, security, and operations teams
- Drive long-term technical vision and roadmap for backend integrity systems
- Mentor engineers and champion engineering best practices across the organization
What they're looking for
- Backend system design and distributed systems architecture
- Python, Go, Java, or C++ programming
- Machine learning lifecycle (data preparation, feature engineering, model training, deployment)
- Content classification and anomaly detection
- Generative AI safety and bias mitigation techniques
- Problem-solving in ambiguous, complex technical environments
- Technical leadership and cross-functional communication
- System optimization and performance analysis
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Character.AI
Character.AI builds an interactive entertainment platform powered by AI characters that serves millions of users monthly, enabling multimodal interactions through advanced video, image, and language generation. The company is hiring research engineers, backend software engineers, ML specialists, and monetization experts to develop core infrastructure, scaling AI safety systems, and building reliable payment platforms.
View all jobs at Character.AILikely interview questions
- Tell us about a time you designed and deployed a large-scale distributed system for integrity or safety. What were the key architectural decisions, and how did you ensure it could handle real-time threat detection at scale?
- Describe your experience applying machine learning to trust & safety problems. Walk us through a specific project—from problem definition through model deployment—and how you measured its real-world impact.