Software Engineer, Backend/Applied ML (Safety & Integrity)

Benefits

Parental leave

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Non-birth-parent leave

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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About this role

Software Engineer, Backend/Applied ML (Safety & Integrity) Redwood City, CA, Redwood City, California, United States About the role We're looking for a talented and creative Software Engineer to join our Safety Engineering team at Character.AI ! In this role, you will be at the forefront of designing, developing, and scaling robust backend systems and leveraging applied machine learning to tackle critical integrity and safety challenges. You will be architecting and implementing innovative solutions in a key role in addressing the unique safety challenges that come with human-to-AI interaction-bringing your technical expertise to the table as we define industry best practices in this emerging space. This is a high-impact role where you will provide technical leadership, drive innovation, and contribute to the core of our platform's trustworthiness. What you'll do - Architect & Build: Design, develop, and maintain highly scalable, resilient, and performant backend systems that power our integrity and safety features. - Lead Complex Solutions: Lead the technical design and implementation of sophisticated backend solutions for detecting, preventing, and mitigating a wide array of integrity risks. This includes traditional issues (e.g., content classification, spam, etc.) as well as emerging threats related to Generative AI (e.g., misuse of generative models, generation of harmful or biased content, etc). - Apply Machine Learning: Conceptualize, develop, deploy, and iterate on machine learning models and algorithms to address complex integrity challenges. This includes areas like content classification (including AI-generated content), anomaly detection, risk scoring, behavior analysis, and developing safeguards for Generative AI systems (e.g., robust

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