Remote Engineering Manager, Resilience & Chaos

Affirm, Inc.; Atlanta (GA); Remote; USD 204,000 - 290,000; Full time

Job summary

Affirm, Inc. is seeking a seasoned Engineering Manager to lead the Resilience Engineering team, ensuring safety and reliability of production systems through production load testing and chaos engineering.

You will develop platforms and tooling for safe production experiments and embed resilience validation across the software lifecycle. You will mentor engineers, partner with infra, product, and security leadership, and establish best practices for testing system limits and failure scenarios in

Qualifications

• Proven experience leading engineering teams in reliability, infrastructure, or distributed systems.
• Hands-on experience with production load testing, chaos engineering, or large-scale system validation.
• Experience with leveraging a chaos engineering vendor such as Gremlin, Harness, or something similar.
• Strong understanding of failure modes in distributed systems, including latency, partial failure, and cascading outages.
• Experience building or operating systems with strong safety guarantees (isolation, rate limiting, guardrails, auditability).
• Familiarity with cloud-native environments (AWS, Kubernetes) and observability tooling.
• Strong programming background (e.g., Python, Kotlin, Java, or similar).
• Excellent problem-solving skills and the ability to balance long-term resilience investments with immediate business needs.
• Strong communication and leadership skills, with a track record of influencing engineering practices across teams.
• This position requires either equivalent practical experience or a Bachelor’s degree in a related field.

Responsibilities

• Define and drive the vision for resilience engineering at Affim, with a focus on production load testing and chaos engineering as first‑class engineering practices.
• Lead and mentor a team of engineers building platforms and tooling for safe production experimentation.
• Partner with infrastructure, product, and security leadership to embed resilience validation into the software development lifecycle.
• Establish best practices for safely testing system limits and failure scenarios in production.
• Own the design and evolution of platforms that enable safe, controlled production load testing and fault injection.
• Ensure strong safeguards are in place, including isolation boundaries, approval workflows, and automated rollback mechanisms to protect real users.
• Build systems that provide end-to-end observability, traceability, and auditability for all resilience experiments.
• Drive reliability improvements by systematically identifying weaknesses through load testing and chaos experiments.
• Establish monitoring, alerting, and incident response practices tailored to proactive resilience validation.
• Work closely with engineering teams to design and execute production load tests and chaos experiments safely.
• Partner with infrastructure teams to build guardrails around tests and experimentations.
• Enable teams to adopt resilience practices by providing reusable tooling, frameworks, and standardized workflows.
• Identify systemic weaknesses and lead cross‑functional efforts to improve reliability and fault tolerance.
• Evangelize a culture of “test failure before failure tests you” across the organization.

Skills

Leadership

Reliability engineering

Chaos engineering

Production load testing

Distributed systems

AWS

Kubernetes

Python

Java/Kotlin

Communication

Education

Bachelor’s degree or equivalent practical experience

Tools

Gremlin

Harness

Job description

Affirm, Inc. is seeking a seasoned Engineering Manager to lead the Resilience Engineering team, ensuring safety and reliability of production systems through production load testing and chaos engineering.

You will develop platforms and tooling for safe production experiments and embed resilience validation across the software lifecycle. You will mentor engineers, partner with infra, product, and security leadership, and establish best practices for testing system limits and failure scenarios in