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

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 integrity risks, including traditional issues and emerging Generative AI threats.
• Apply Machine Learning: Conceptualize, develop, deploy, and iterate on machine learning models for content classification, anomaly detection, risk scoring, behavior analysis, and Generative AI safeguards.
• Cross-Functional Collaboration: Work with product managers, data scientists, AI researchers, security teams, and operations to define requirements and deliver impactful systems.
• Technical Strategy & Roadmap: Drive the long-term technical vision for backend integrity systems and applied ML, aligned with company objectives.
• Mentorship & Leadership: Provide technical guidance and mentorship to engineers.
• Champion Best Practices: Implement best practices in software engineering, distributed systems, data engineering, and ML lifecycle with focus on Generative AI safety.
• System Optimization: Analyze and improve performance, scalability, reliability, and cost-effectiveness of platforms and models.
• Stay Current: Keep up with emerging threats, technologies, and advancements in backend engineering, ML for trust & safety, and Generative AI safety.

Who you are

• 8+ years of professional software engineering experience, with strong emphasis on backend systems development.
• Bachelor's, Master's, or PhD degree in Computer Science, Engineering, or related technical field.
• Proven track record of designing, building, and operating complex, large-scale, highly available distributed systems.
• Expertise in one or more backend programming languages such as Python, Go, Java, or C++.
• Hands-on experience applying machine learning to real-world problems, especially integrity, trust, or safety challenges.
• Solid understanding of the machine learning lifecycle, including data gathering/cleaning, feature engineering, model selection, training, validation, A/B testing, deployment, and monitoring.
• Exceptional problem-solving abilities for ambiguous challenges.
• Proven ability to work in fast-paced environments and deliver timely results.
• Strong communication, interpersonal, and leadership skills.

You will be a great fit if:

• You care deeply about Trust & Safety.
• Prior experience in Trust & Safety, Integrity, or Risk engineering team.
• Contributions to open-source projects or publications.
• Experience leading large, cross-cutting technical projects.