Principal Software Engineer, Anti-Abuse

As a Principal Software Engineer, Anti-abuse at Snowflake, You Will

• Define and drive the long-term technical vision for abuse and fraud detection across the Snowflake platform, serving as the chief architect for platform-wide anti-abuse strategy
• Partner directly with product and engineering teams building Snowflake's newest and highest-risk surfaces (Snowpark user code execution runtimes, Data Sharing and Marketplace, AI/LLM services) to design and deploy robust anti-abuse controls from day one
• Architect and scale high-throughput, low-latency telemetry pipelines and risk-scoring engines capable of analyzing millions of queries, sessions, and data transactions across multi-cloud environments (AWS, Azure, GCP)
• Continuously model emerging abuse techniques, including trial compute abuse, cryptocurrency mining, bot-driven account creation, credential stuffing, and illicit network traffic routing
• Design intelligent mitigation systems that automatically throttle, challenge, or isolate abusive workloads and malicious actors with near-zero false-positive impact on legitimate enterprise workloads
• Raise the technical bar across the engineering organization through mentorship of staff and senior engineers, and influence architectural standards across adjacent platform teams

Our Ideal Principal Software Engineer, Anti-abuse Will Have

• 12+ years of software engineering experience in distributed systems, cloud platform infrastructure, or security engineering, with significant time dedicated to anti-abuse, fraud detection, platform trust, or threat mitigation
• Proven track record architecting mission-critical, large-scale distributed services in Java, C++, or Python
• Deep experience designing controls for code-execution runtimes, multi-tenant compute environments, cloud APIs, or data platforms (e.g., mitigating compute hijacking, sandbox escapes, abuse of serverless functions, or network egress abuse)
• Demonstrated ability to build streaming event processing, anomaly detection, behavioral heuristic engines, or graph-based syndicate detection systems at enterprise scale
• Strong intuition for attacker economics and mechanics, with experience modeling threat vectors across account lifecycles (signup, trial, payment, execution, sharing)
• Exceptional ability to partner with product managers, infrastructure teams, and security researchers to embed safety controls into high-velocity product roadmaps
• B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, Cybersecurity, or equivalent practical experience

Bonus Points for the Following

• Experience building anti-abuse systems for multi-tenant cloud platforms or SaaS-at-scale environments
• Familiarity with AI/LLM abuse vectors (prompt injection, model extraction, automated content generation for fraud)
• Track record of designing zero-trust or defense-in-depth architectures for compute runtimes
• Published research or industry contributions in fraud detection, adversarial ML, or platform trust and safety
• Experience operating detection and response systems across multiple cloud providers simultaneously