Chainguard

Overview

We're hiring a Staff Software Engineer to lead the engineering of our shared malware scanning platform. You'll own its architecture, scale, and reliability, partnering closely with Product Security to turn threat research into fast, accurate detections.

About Chainguard

Chainguard is the trusted source for open source. By delivering hardened, secure, and production-ready builds of all the open source software engineers and AI agents rely on, Chainguard helps organizations build faster, stay compliant, and eliminate risk.

Requirements & Eligibility

• Multiple years building and operating production backend or infrastructure systems
• Clear track record of staff-level ownership and technical leadership
• Strong Go experience, or deep backend systems experience with the ability to ramp quickly in Go
• Experience owning highly technical platforms or backend infrastructure that supports multiple products or internal customers
• Experience with high-throughput, event-driven pipelines where throughput, latency, and correctness all matter at once
• Strong understanding of software supply chain security, malware detection, vulnerability management, or adjacent security domains
• Demonstrated success making engineering design and prioritization decisions in technically complex and ambiguous environments
• Comfort owning a metric like false-positive rate, instrumenting it honestly, and driving it down

Key Responsibilities

• Build the measurement behind coverage and precision: the pipelines, metrics, and dashboards the product is steered by
• Engineer the feedback loop between Engineering and Product Security so a detection change can be evaluated and shipped in hours, not days
• Build the systems for reviewing, escalating, and correcting detections quickly, including bulk correction at ecosystem scale
• Own the architecture of Chainguard's shared malware scanning platform: scan orchestration, verdict storage, and the APIs every consuming product depends on
• Scale the scanner beyond Libraries to Containers, Agent Skills, and future artifact types
• Make the tradeoffs between detection quality, performance, and extensibility concrete in throughput, latency, and cost
• Build and scale the analysis itself: deterministic static analysis alongside AI-assisted reasoning over artifact contents
• Partner with Product Security to take emerging-threat detections from research prototype to production