Software Engineer, Trust & Safety (Distributed Systems)

• Own detection and prevention systems for fraud, abuse, spam, and malicious content across millions of daily users
• Design and build scalable trust infrastructure including rate limiting, content scanning, anomaly detection, and account security
• Architect distributed systems, APIs, and high-volume event pipelines that power real-time abuse detection at Gamma's scale
• Help define and evolve the core data model and storage systems that underpin trust and safety across the product
• Build tools that empower internal support teams to investigate and act on suspicious or malicious activity
• Leverage AI/LLM-based detection to stay ahead of AI-generated abuse — phishing, fraud, and malicious content that's increasingly indistinguishable from legitimate activity
• Build automated triage and investigation workflows that let a small team operate at the scale of a much larger one
• Investigate and resolve complex security incidents with limited context
• Partner with engineering and product to balance security with user experience
• Shape Gamma's long-term trust and safety strategy and technical roadmap

• 5+ years of backend engineering experience building scalable, high-traffic production systems
• Strong systems thinking and experience building highly available web APIs
• Strong proficiency in backend technologies (Node.js, Python, or similar) and databases (PostgreSQL, Redis)
• Experience with event streaming systems (Redis, Kafka, or similar) and high-volume event pipelines
• Hands-on experience implementing trust features like rate limiting, content detection, and fraud prevention
• Track record shipping high-quality, complex applications under tight timelines
• Product-minded approach with understanding of how technical decisions impact user experience and business metrics
• Passion for security, user protection, and solving problems at scale

• Experience with AI/LLMs for content moderation, or familiarity with TypeScript, Prisma, Apollo GraphQL, or AWS
• Experience building AI-assisted moderation pipelines or using frontier models for automated abuse detection and response
• Familiarity with the evolving AI threat landscape — how generative models are being used for phishing, social engineering, and content abuse at scale
• Experience with real-time collaboration systems

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