Software Engineer II - Platform Anti-abuse

Klaviyo Klaviyo · Enterprise · Boston, MA · Engineering

Software Engineer II on the Platform Anti-Abuse team at Klaviyo. This role involves building and operating backend services for fraud and abuse detection, integrating ML classifiers, and extending enforcement capabilities across product surfaces. The focus is on defending against real-world adversaries at scale using a combination of rules, signals, and ML.

What you'd actually do

  1. Own features end-to-end across design, implementation, rollout, and observability for abuse detection and enforcement capabilities; rules, classifiers, enforcement pipelines, and the platform services that other teams plug into.
  2. Extend abuse enforcement to new product surfaces: Help bring anti-abuse rules and enforcement to product areas where coverage is currently limited, ensuring consistent policy application across channels.
  3. Build content and link abuse detection systems: Help design and implement detection capabilities for malicious URLs, abusive image content, and other content-level signals;combining perceptual hashing, ML model integrations, and rule-based approaches.
  4. Contribute to AUP automation and enforcement pipelines: Help build and scale the systems that automatically evaluate accounts against our Acceptable Use Policy, reducing reliance on manual compliance review.
  5. Improve platform anti-abuse infrastructure: Evolve microservices, rules orchestration layer, and abuse observability pipelines; making them faster, more reliable, and easier for the team and compliance stakeholders to operate.

Skills

Required

  • 2-5+ years of professional software engineering experience
  • building and operating backend or full-stack services in production
  • Strong fundamentals and debugging skills
  • reasoning about data models, API design, concurrency, and failure modes
  • dig through logs, metrics, and traces to identify root causes and implement systemic fixes
  • Platform and signal mindset
  • Ownership and collaboration

Nice to have

  • Go services
  • Python rules pipelines
  • ML-based classifiers for content and URL abuse
  • perceptual hashing

What the JD emphasized

  • Fight real adversaries at scale
  • Your work is the platform's safety net
  • Work at the intersection of rules, ML, and systems engineering
  • Automate policy enforcement at scale
  • building defenses against real-world fraud and abuse
  • Security and abuse motivated
  • Platform and signal mindset
  • shared enforcement APIs and detection pipelines

Other signals

  • ML-based classifiers
  • rules, signals, and ML
  • content and link abuse detection systems
  • ML model integrations